1 Big Pitfall to Successful Demand Generation Digital Transformation

As marketing leaders, we sometimes inadvertently lead our teams astray. When we delegate the outcomes we want, and simultaneously drive a sense of urgency, our teams may skip important steps in their drive to achieve the outcomes.

As marketing leaders, we sometimes inadvertently lead our teams astray. When we delegate the outcomes we want, and simultaneously drive a sense of urgency, our teams may skip important steps in their drive to achieve the outcomes. Here is a classic example we see all too frequently with clients.

The Scenario

We start with the desired outcomes, of course. In demand generation, this is usually marketing-qualified leads (MQLs) or sales-qualified leads (SQLs), bookings and revenue. If this desired outcome was somehow unexpected, then a sense of urgency invariably accompanies it. So, in turn, we light a fire under our teams to quickly get some leads in the door, generate MQLs and SQLs ASAP.

The Result and the 1 Big Pitfall

We need to generate leads and MQLs? Let’s create a campaign! Yay! Design it this week, build it next week, QA and launch the end of the week, and leads will start pouring in subsequent to that. Oh, dear. If only it were that easy. Going straight from “we need MQLs” to “let’s create a campaign” means going from Step 1 to Step 8 and skipping six important steps.

  1. Generate new MQLs and SQLs
  2. Create a campaign!

Here are the six intervening steps you will want to ensure your team takes if you are to have successful demand generation campaigns and succeed in your digital transformation.

Preventing the Pitfall

Step 2. What Buying Stage Transition Are We Targeting?

Once we understand the outcome desired in Step 1, we must determine what customer buying journey stage we are targeting. Are we moving people from unaware to aware, or from aware to consideration, etc. If you haven’t defined the customer buying journey, stop and define at least one.

Step 3. What Persona Are We Targeting?

Don’t have any defined personas? Stop and define at least one. Having a clear picture of who you are targeting is a critical step to successfully achieving your outcome. Now that you have the persona selected, the team gets to review what channels the persona prefers, and the content preferences. Step 2 and Step 3 are interchangeable. I.e., there will be occasions where you perceive your funnel conversion rate from one stage to another is low, and you make the buying journey stage decision first. There will be other times where you recognize your funnel volume is low on a particular persona, and you make the persona decision first and the buying journey stage decision second. Regardless, you need to take both steps.

Step 4. What Problem Does Your Persona Have at This Stage?

The next question to answer, now that you have selected the target persona, and the current buying journey stage, is what problem do the members of it have at this stage that can be solved with your content?

For example, if you are targeting the Aware stage, and want to move them to Consideration, what information or education will trigger the buyers to sit up, realize they have been ignoring a pain in their sides that is curable, or that they have an opportunity to do something they have not done before, and they need to finally take action? The ideal content you send is most likely NOT product-centric. It will be customer-centric and it will have the buyer‘s challenge or the opportunity as the primary theme. It will be very narrowly focused around that theme.

We are looking for the trigger that will move this persona one step forward in the buying journey. We are not trying to move them all of the way to “closed won” with a single piece of content, or a single campaign.

Step 5. What Message or Content Addresses That Challenge or Opportunity?

Okay, we have the target persona, the buying journey stage they are in, the trigger we feel will tip them forward into the next stage in the buying journey. So now the question is, what content do we have that directly addresses this issue? Ignore the medium it is in for now, as repackaging it may not be hard. Focus on which Subject Matter Experts (SMEs) can or have already produced in terms of educational pieces of content that will be most effective in engaging the targets and moving them forward.

Step 6. What Is the Appropriate Medium for the Information?

All too often, we have clients ask us: “What is the hottest medium to use these days — video, white papers, webinars, slide shares, infographics, what?”

This is totally the wrong question! The answer depends on the message itself, and the persona and, to some extent, the buying stage they are in.

For example, if your target persona is a technical influencer, and uses a smartphone frequently to read email on the commuter train in the morning, sending a white paper would be silly, but a 2-minute video could work great … depending on the message you are trying to send. And the medium may also depend on the channels we pick in Step 7. Because more and more campaigns are becoming multichannel, it is likely you will end up choosing multiple media for the message, to match the multiple channels you use to engage the targets.

Step 7. What Combination of Channels Will We Use to Communicate That Content?

Next, we have to determine which channels will work for this persona. It is a good idea to use more than one channel to convey the same message to the same individuals. The results will simply be better, and the level of effort is not significantly more.

Some firms erroneously believe that paid media ads are only for top-of-funnel, new-lead acquisition. This is not true.

For instance, you can upload a list of email addresses into Facebook, or LinkedIn, match them against their data to create your new target list, and then do nurturing campaigns through those channels very economically only to your existing leads.

Step 8: Put It All Together

Now you are finally ready for Step 8. Let’s design a campaign based on all of the decisions made in Steps 2-7.  Now the cynics among you will say, “Hey, steps 2-7 are really part of basic campaign design, how can people be skipping them?”

The Pitfall You Just Avoided

Well, many firms don’t have defined personas and buying journey maps and here is what happens:

Step 1. CMO: We need more MQLs, urgently

Step 8. Team: Let’s design a campaign

  • An Email campaign, right? Blast everyone who is not a customer in our database, right?
  • 4 touches, check
  • 2 weeks apart, check
  • What offers can we put in there, a case study, an infographic, a research report and the last email is the call to action — “request a demo.” Check
  • Great, code up that campaign, we can get this out in under two weeks. Yay.
  • Count all the MQLs.

Conclusion

So, the message is this. If you urgently discover you need more MQLs, update your resume, not your campaign calendar. If you want to be successful in digital transformation, become more customer-centric, and approach customer engagement from the buyer perspective:

Think about what information they need first. Secondly, determine what content contains that information and then lastly, what channels and campaigns can convey that information to the recipients. And understand that one campaign does not produce a meaningful flow of MQLs or SQLs. Nurturing is a process, it requires commitment and it must be sustained over a longer period of time

Is Speed Dating a Viable Marketing Strategy During Digital Transformation?

Embarking on a digital transformation can be compared to adopting a speed dating strategy. You might “meet” a whole lot of prospects a whole lot faster, but if your behavior is product-centric instead of customer-centric, you’ll simply succeed in inoculating a lot more people to your charms a lot faster.

Imagine you have a friend who has had no luck at dating. Instead of looking into the reasons why they’ve had bad luck and changing their behavior, they tell you they’re going to start speed dating. Twenty dates a night! Surely they’ll have some luck! But the same behavior, 20 times faster, means 20 times the same results — even in marketing strategy. Embarking on a digital transformation can be compared to adopting a speed dating strategy.

You might “meet” a whole lot of prospects a whole lot faster, but if your behavior is product-centric instead of customer-centric, you’ll simply succeed in inoculating a lot more people to your charms a lot faster.

A marketing digital transformation requires deploying, adopting, and coordinating the technologies and programs to enable you to communicate digital content over digital channels with your customers and prospects. The behavior change that must go hand-in-hand with digital transformation is that of becoming customer-centric in how you engage, and the content with which you engage.

Why Customer Experience Drives Success

Take Uber and Lyft, for example. Cars with drivers still take you from A to B in exchange for money. So it’s the same service as regular taxis, right? Wrong. If all Lyft or Uber did was enable you to digitally order up a cab with your smartphone, it really wouldn’t have changed the customer experience. But Lyft and Uber disrupted the transportation industry by changing the ordering, the visual tracking of the vehicle, the payment, the tipping and the rating of the drivers. They changed the entire customer experience, and ultimately bankrupted the Yellow Cab Company. These weren’t direct outcomes of a digital transformation; they were the outcomes of building a business that put customer experience first. Digital transformation was a means to that end.

The point is that we need to embark on a digital transformation and decide the aspects of it we wish to prioritize, based on the customer experience we want to achieve and the behaviors of our company we therefore need to support. And if you thought deploying digital technology was hard, try changing behaviors!

Pop Quiz: Are You Customer or Product-Centric?

How do you know if your customer-perceived behavior is customer-centric or product-centric? Here’s the pop quiz:

  1. Is your website organized primarily by product/services/solutions?
  2. Does your site include more pictures of products or satisfied customers?
  3. Does your 1-800 number ring through to a phone tree or a human being?
  4. Can the service rep see your entire customer record while on the phone?
  5. Do you have a preference center?
  6. Do you segment your communications based on where people are in their buying journey?
  7. Do you use personas for segmentation?
  8. Do you plan and develop content based on personas and prospect information needs at each stage of the customer buying journey?
  9. Can your sales development reps (SDRs) and sales reps see all of the digital interactions prospects have had with your company?
  10. Does marketing have a defined role in the onboarding of new customers?
  11. Do you identify and treat loyal customers differently?
  12. Do you have reports and dashboards that measure marketing performance after the close, including onboarding, adoption, value delivery, loyalty and advocacy?
  13. Do you have an executive responsible for customer experience?
  14. Do you measure the quality of customer experiences other than by revenue?

This list should make it clear that getting to great customer experiences is much more complicated than fiddling with GUIs. It is a company-wide initiative, where marketing has a leading role. Marketing’s job is to help customers and prospects buy more by delivering great customer experiences in all stages of the buying journey.

WARNING: There will be plenty of resistance to this behavior change.

Embrace Customer Intimacy

Twenty years ago, I spoke with the CIO of one of the largest video store chains in NA. I asked him why they didn’t cut a deal with the USPS to allow customers to return the videos for free via mail, because they had sturdy plastic cases with the store address on them. His response was that a majority of their profit came from “late returns,” so they didn’t want to change it. I shared with him that a profitability model predicated on a bad customer experience would not end well. Today, all 6,000 stores are closed.

So don’t be one of those firms that thinks deploying a marketing automation platform or email platform empowers you to spam 100,000 people with one click. Don’t dream that if you build a product- centric website “they will come.” Don’t inject yourself into social media conversations with self-promoting materials. Don’t believe that marketing technologies are narrowly focused lead generation.

Instead, decide what improvements you can make to the customer experience this year, and plan changes to your behaviors in marketing, sales, support, operations and finance. That will drive the digital transformation requirements and priorities and prove that blindly deploying martech will not lead to better dates.

Read more about operationalizing the customer experience.

Think of Customer Experience as a Marketing Investment

As products and services become commoditized, organizations need to begin differentiating themselves by becoming customer-centric and providing a consistently good customer experience. The bar is low; it’s pretty easy to stand out from your competition if you just make a commitment to do so.

As products and services become commoditized, organizations need to begin differentiating themselves by becoming customer-centric and providing a consistently good customer experience. The bar is low; it’s pretty easy to stand out from your competition if you just make a commitment to do so.

Here are eight reasons for your organization to invest in customer experience:

  1. Price Isn’t the Only Differentiator. People will pay more for excellent customer service and a great customer experience. In fact, American Express found consumers are willing to spend 17 percent more to do business with companies that deliver excellent customer service.
  2. It’s Not That Hard to Improve Level of Customer Service you provide and improve the customer experience of your customers. It does take commitment, focus, determination, measurement and listening.
  3. Happy Customers Are Good Customers. They buy more, they buy more frequently and they tell their family, friends and colleagues about your products, service and their customer experience. And referrals and word of mouth are still the most cost-effective marketing you can get.
  4. CX Doesn’t Require Leading-Edge Software. However, it does require good customer relationship management (CRM) software and a commitment by everyone in the firm to use it and listen intensely to what the customer is saying and how your organization makes them feel.
  5. It’s Cheaper to Retain Current Customers Than Acquire New Customers — some studies suggest by a factor of seven.
  6. Any Company of Any Size Can Provide Consistently Excellent Customer Service and “wow” customer experiences. It’s a customer-centric attitude that starts at the C-level and cascades down to everyone in the organization.
  7. Happy Customers Find New Customers for You. They provide referrals, testimonials, they share their positive thoughts and experiences with family, friends and colleagues, and they post on social media sites.
  8. Improving CX Pays for Itself. Think of providing good customer service as a marketing investment.

Most companies provide lousy customer service and a negative customer experience. CX is a great way to differentiate your firm from your competition. A customer who has an issue that is resolved is more likely to become a long-term customer and spend more with you over time, than the customer who doesn’t complain. Providing great customer service and a “wow” customer experience can help create “raving fans” who will sing your praises to family, friends, colleagues, and even strangers via the Internet and social media.

A dissatisfied customer leaves and tells their friends, and possibly many others, about what a poor job you did. As such, you’re much better off resolving the issue to the customer’s satisfaction.

Use simple math to convince the CEO to bring marketing and customer service together.

Listen intensely to learn customers’ needs and expectations.

Empower everyone in your organization to provide outstanding customer service to end-user customers and colleagues.

Attitude is everything. When every employee considers themselves part of the customer service team, your company is able to deliver a level of customer service that’s a competitive differentiator for your firm.

Pay back customers for their business with excellent customer service. Your customers will become “raving fans” and will evangelize your brand.

5 Marketing Capabilities for Customer-Centric Digital Transformation

A couple of months ago we discussed what marketing capabilities are needed for a digital transformation. Let’s now address the additional capabilities required to transform a traditional marketing organization into a modern revenue marketing machine.

A couple of months ago we discussed what marketing capabilities are needed for a digital transformation. We narrowed the scope of the answer to just technology-related capabilities. Let’s now address the additional capabilities required to transform a traditional marketing organization into a modern revenue marketing machine.

“A capability is a unique bundling of skills, knowledge, and resources that facilitate the execution of business processes, and are what ultimately contribute to sustainable competitive advantage and superior performance.” (Day, 1994).

5 Core Customer-Related Capabilities Marketing Must Acquire:

  1. Buying Journey Management is the capability that maximizes the sales and marketing activity with its buyers at all stages of their buying lifecycle, resulting in stronger customer relationships, increased revenues, profit and competitive advantage. This buying journey repeats itself with every purchasing decision. This capability is not simply about defining the buying journey; it is about aligning your content and marketing activities so they align with where the buyers are in their journey. Have you defined your customer journey? Have you mapped your content to the journey?
  2. Content Operations is a capability that supports the production, collection, management, publishing and measurement of customer or prospect-oriented information in any form or medium. Marketing manages content to support successful execution and optimization of multi-channel programs and campaigns. Content operations is a factory that collects requirements from demand generation, product marketing, and sales. They publish a production calendar. They are experts at content curation and understanding the best media for each message by segment.
  3. Customer Engagement is the capability that maximizes the relationships between sales/marketing and prospects/customers through the stages of the customer lifecycle. Too many firms simply pour out blog posts, newsletters, and promotional emails without actually measuring the level of engagement they are driving with each touch. Achieving a high level of engagement with your content and offers requires deep understanding of what messages and content works with which persona in which part of their buying journey, through what channel, and in what medium. The expectation is good engagement produces good conversion rates in your funnel. How good is your team at engaging prospects? Do you measure it?
  4. Customer Knowledge Management is a cross-functional capability for collecting, organizing, sharing and gaining insight from market and customer information, which drives stronger customer relationships and results in increased revenue, profit and competitive advantage. Back in the day, when direct mail was king and email was a novelty, we said the success of a campaign was 60% predicated on the quality of the data, 20% on the quality of the offer, and 20% on the quality of the packaging. We may have shifted the bulk of our campaign communications to digital channels, but the rule still applies. If your customer data quality is poor, don’t expect campaign miracles. Your customer data is an expensive asset that you have acquired. It depreciates at 2.1% per month (source Marketing Sherpa). Effective management of your customer data is a core capability in digital marketing success.
  5. Persona Management is the capability that develops, manages and optimizes semi-fictional characters to represent the different customer types that might use a company’s products or services. Personas need to go beyond job titles in B2B, because otherwise you are simply describing a segment. Modern marketing technologies enable us to gather, assess and adapt to people’s behaviors. Good personas therefore reflect the person’s goals, needs and decision-making behavior. Persona management means you recognize that personas are not static and need constant updating and refinement.

Delivering on each of these capabilities requires certain skills, knowledge and experience. You can outsource some of these capabilities to agencies or train your internal team to fulfill them.

4 Steps to Digital Transformation by Customer Capability Acquisition

  1. Assign someone to be your data czar, even if it is only part of their role. Have them create data quality dashboards.
  2. Define one or more customer journeys with the help of sales and map your content to the journey stages. What are the gaps? Build a plan to eliminate the content gaps. Create a content calendar. Now start planning your engagement based on the journey stages.
  3. Start measuring customer engagement with your content, events, offers, ads and all digital properties. What content is driving more engagement or minimal engagement?
  4. Finally, define personas, map the content to the personas, and start to collect data on your prospects so you can learn their persona and start to improve how you communicate with them!

Customer experience is the new competitive battleground. Shift to greater customer centricity in 2019 by investing now in the five marketing capabilities described above.

The Art of Data Categorization

Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. My dog has a Facebook page — don’t ask why — and on his last birthday, Facebook correctly converted his age to dog years.

Do you know why some reports are unbearably long and filled with numbers that are irrelevant to decision-making? It is mostly because there are serious misalignments between the desired level of detail in reporting and actual data categorization. Raw data, with very few exceptions, are rarely ready for decision-making (through various reports) or statistical modeling (an important part of what we often call advanced analytics).

Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. My dog has a Facebook page — don’t ask why — and on his last birthday, Facebook correctly converted his age to dog years. I kept hearing that machines have a hard time separating dog and cat pictures, but apparently such an obstacle has been overcome (or do they just use dog years for cats, too?).

In any case, do machines understand the “purpose” of categorization and tagging, as well? Does it understand why it is even necessary to put my dog’s age in dog years? That is an entirely different matter, and whether the work is done by humans or machines, I have seen time and time again that categorization efforts with clear purposes result in improvement in analytics and prediction.

Let’s take an example of the hot topic of personalization. Folks who have read my previous articles may already know that I am not even nearly impressed with various marketing efforts under the banner of personalization today. Most are done on a product level, with raw product-level data, when the personalization must foremost be about the person.

Even at a basic level of personalization, consumers on the receiving end often suspect that some personalization engines don’t even consider categories of products, as a suggested product is often irrelevant, dubious or even stupid (as in, “Hey, I just bought that exact item! Why are they offering it to me again?”). I can think of many reasons why that happens (mostly around data and analytics), but the first wrong gear often is that data are not properly categorized.

Results of analytical efforts for personalization and other complex challenges certainly improve when clean data enters the system. The reasons why most analysts spend the majority of their valuable time in data preparation — or even give up to use some granular data — is mostly because input data are unclean, unstructured or uncategorized.

Allow me to share some categorization rules that I have developed based on countless trials and errors during my co-op database days, when we had to put tens of millions of SKUs from over 1,500 sources into one consistent list of categories, solely for the purpose of analytics for individual-level targeting. Whether the actual categorization is done by humans or machines is not the issue; they all have to “learn” what the proper category is to be assigned for each item, and that starts with a proper categorization framework.

The rules I am introducing here are for personal-level targeting and customization of messages; therefore, “customer-centric” at the core. You may need to develop separate frameworks, if the goals are different. Problem statements such as “What will be the most popular product next season?” for instance, would require product-centric categorization. Nonetheless, this framework will be useful when setting up your own, as well.

Without further ado, let’s dive into the list:

Categorize the Buyers, Not the Product

This may not sound intuitive, but it is the first item to remember when setting up a goal-oriented categorization framework. If it is for personalization, and if you are creating a 360-degree view of customers for that purpose, don’t stop there and convert the product-level information into descriptors of buyers. And categorizing items with this goal in mind results in a vastly different — and far more predictable — outcome.

For instance, some items in a nautical catalog, such as a wall-mounted weather station (displaying temperature, air pressure, humidity, etc. on a fancy panel), can also be purchased from an executive gift catalog or website. When assigning categories for items like that, think about the context of the purchase, not just SKU descriptions, to avoid cases where you end up sending nautical catalogs to casual gift buyers. When in doubt, imagine how many purposes baking soda serves; think about the context of the purchase to describe the buyer, depending on the specific purpose (e.g., baking, personal hygiene, deodorization of a refrigerator, domestic cleaning, etc.).

Also consider the price scale and purpose of the purchase, so that you do not end up putting a cheap, everyday lamp and a state-of-the-art home decor lamp in the same category, leading to seriously misaligned offers. You must look beyond simple product descriptions.

The More Specific, the Better

Basically, don’t be lazy and put a 4K TV under “Home Electronics” and call it a day. For apparel items, gender break is the easy part, but sub-categories are even more important for prediction. Most modern product categorization schema are multilevel, like Home Electronics>Home Theater>TV>4K TV. So use it fully.

I’m not saying that all the minute details are helpful for analytics; I’m just emphasizing that one can combine categories later in the process. But if things are lumped up to begin with, one cannot break them apart without resorting back to the source data.

You will be better off if this type of effort is performed as early in the process as possible. Don’t create some big homework for everyone — especially for the analysts — for later. 

Consistency Over Accuracy

This may sound weird as well, but consistently wrong data may be more predictable than “sometimes” accurate data. Assigning the same item to multiple categories creates all kinds of havoc in reporting and prediction downstream. We may argue forever if a certain type of luxury handbag belongs in a category, with no clear winner in the end. The key point is that one should not go back and forth with established categorization rules.

If you can’t settle the fight, then use multiple tags for an item (I don’t recommend it personally). In any case, to machines and algorithms, those categories are just a numeric representation of where they belong, without any judgement. Don’t spend too much energy on making human sense out of every assignment. We can always change the “label” at the reporting stage.

Categorize Only as Much as It Matters

When categorizing items for targeting and reporting, we do not have to create a new schema that covers the entire spectrum of items. If targeting is the end-goal, you don’t even have to touch the items that did not sell very well, as there are not many buyers behind them. Going further, it is alright to categorize the top 20 percent of the items in terms of popularity (i.e., number of transactions or revenue dollar amount), if it covers over 80 percent of the customer behaviors. Yes, I said don’t be lazy under No. 2, but there is no point in spending energy categorizing small items that may not even move analytical needles later. In other words, know when to stop and use the “All other” category for insignificant ones.

Cut Out the Noise

Not every little detail matters in analytics. For example, the “color” of an item may matter a great deal for inventory management (as in “Hey, we are running low on the toasters in Ferrari red!”). But unless you are thinking about targeting people who only purchase items in red, you may not need such details for customized communication and offers. Break down the elements that make up an item, and go only as far as your specific goal calls for. Consult with analysts when in doubt.

Be Inventive

Creating the category buckets is the first important step of categorization efforts. This is where one must “imagine” what type of category would be useful for reporting and prediction later. Simple food labels could lead to all kinds of interesting “behavioral” categories that may be extremely useful when personalizing offers (refer to “Freeform Data Are Not Exactly Free”). This may sound contradictory to No. 5, but hitting the right balance between “too much” and “too little” is indeed the human function — for now — that I was talking about.

Conclusion

Analytics, as we’ve been saying for a long time, is a “garbage-in-garbage-out” business. But in the age of abundant and ubiquitous data, some “seemingly” useless data can be truly predictable. If we don’t think about “data refinement” — of which categorization is a big part — analysts will end up beating down a few popular variables, or worse yet, push down the raw data through some analytical engine “hoping” for some good results.

If the current state of personalization is any indication, most available data must be refined in more systematic and rigorous fashion, whether done by machines or humans. And until the machine catches up with us in the area of creativity, intuition, as well as logical deduction, we will have to be the ones who set up the framework.

Data became too big and complex and customers became too demanding for marketers to leave anything to chance. Even your off-the-self personalization engine will run better with well-categorized data. So, commit to that step, set up proper frameworks and rules, and move onto automation once the organization is ready for it.

AI may take over the world soon, but different types of thinking machines will have to work together to make various marketing efforts truly fruitful. And categorization, along with predictive analytics, is an important component. That is, if you as a consumer believe that machine-driven personalization can use a “human touch.”

Marketing and IT; Cats and Dogs

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective, first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

 

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

Of course I am generalizing for a comedic effect here, but I see communication breakdowns like this all the time in business environments, especially between the marketing and IT teams. You think men are from Mars and women are from Venus? I think IT folks are from Vulcan and marketing people are from Betazed (if you didn’t get this, find a Trekkie around you and ask).

Now that we are living in the age of Big Data where marketing messages must be custom-tailored based on data, we really need to find a way to narrow the gap between the marketing and the IT world. I wouldn’t dare to say which side is more like a dog or a cat, as I will surely offend someone. But I think even non-Trekkies would agree that it could be terribly frustrating to talk to a Vulcan who thinks that every sentence must be logically impeccable, or a Betazed who thinks that someone’s emotional state is the way it is just because she read it that way. How do they meet in the middle? They need a translator—generally a “human” captain of a starship—between the two worlds, and that translator had better speak both languages fluently and understand both cultures without any preconceived notions.

Similarly, we need translators between the IT world and the marketing world, too. Call such translators “data scientists” if you want (refer to “How to Be a Good Data Scientist”). Or, at times a data strategist or a consultant like myself plays that role. Call us “bats” caught in between the beasts and the birds in an Aesop’s tale, as we need to be marginal people who don’t really belong to one specific world 100 percent. At times, it is a lonely place as we are understood by none, and often we are blamed for representing “the other side.” It is hard enough to be an expert in data and analytics, and we now have to master the artistry of diplomacy. But that is the reality, and I have seen plenty of evidence as to why people whose main job it is to harness meanings out of data must act as translators, as well.

IT is a very special function in modern organizations, regardless of their business models. Systems must run smoothly without errors, and all employees and outside collaborators must constantly be in connection through all imaginable devices and operating systems. Data must be securely stored and backed up regularly, and permissions to access them must be granted based on complex rules, based on job levels and functions. Then there are constant requests to install and maintain new and strange software and technologies, which should be patched and updated diligently. And God forbid if anything fails to work even for a few seconds on a weekend, all hell will break lose. Simply, the end-users—many of them in positions of dealing with customers and clients directly—do not care about IT when things run smoothly, as they take them all for granted. But when they don’t, you know the consequences. Thankless job? You bet. It is like a utility company never getting praises when the lights are up, but everyone yelling and screaming if the service is disrupted, even for a natural cause.

On the other side of the world, there are marketers, salespeople and account executives who deal with customers, clients and their bosses, who would treat IT like their servants, not partners, when things do not “seem” to work properly or when “their” sales projections are not met. The craziest part is that most customers, clients and bosses state their goals and complaints in the most ambiguous terms, as in “This ad doesn’t look slick enough,” “This copy doesn’t talk to me,” “This app doesn’t stick” or “We need to find the right audience.” What the IT folks often do not grasp is that (1) it really stinks when you get yelled at by customers and clients for any reason, and (2) not all business goals are easily translatable to logical statements. And this is when all data elements and systems are functioning within normal parameters.

Without a proper translator, marketers often self-prescribe solutions that call for data work and analytics. Often, they think that all the problems will go away if they have unlimited access to every piece of data ever collected. So they ask for exactly that. IT will respond that such request will put a terrible burden on the system, which has to support not just marketing but also other operations. Eventually they may meet in the middle and the marketer will have access to more data than ever possible in the past. Then the marketers realize that their business issues do not go away just because they have more data in their hands. In fact, their job seems to have gotten even more complicated. They think that it is because data elements are too difficult to understand and they start blaming the data dictionary or lack thereof. They start using words like Data Governance and Quality Control, which may sound almost offensive to diligent IT personnel. IT will respond that they showed every useful bit of data they are allowed to share without breaking the security protocol, and the data dictionaries are all up to date. Marketers say the data dictionaries are hard to understand, and they are filled with too many similar variables and seemingly conflicting information. IT now says they need yet another tool set to properly implement data governance protocols and deploy them. Heck, I have seen cases where some heads of IT went for complete re-platforming of their system, as if that would answer all the marketing questions. Now, does this sound familiar so far? Does it sound like your own experience, like when you are reading “Dilbert” comic strips? It is because you are not alone in all this.

Allow me to be a little more specific with an example. Marketers often talk about “High-Value Customers.” To people who deal with 1s and 0s, that means less than nothing. What does that even mean? Because “high-value customers” could be:

  • High-dollar spenders—But what if they do not purchase often?
  • Frequent shoppers—But what if they don’t spend much at all?
  • Recent customers—Oh, those coveted “hotline” names … but will they stay that way, even for another few months?
  • Tenured customers—But are they loyal to your business, now?
  • Customers with high loyalty points—Or are they just racking up points and they would do anything to accumulate points?
  • High activity—Such as point redemptions and other non-monetary activities, but what if all those activities do not generate profit?
  • Profitable customers—The nice ones who don’t need much hand-holding. And where do we get the “cost” side of the equation on a personal level?
  • Customers who purchases extra items—Such as cruisers who drink a lot on board or diners who order many special items, as suggested.
  • Etc., etc …

Now it gets more complex, as these definitions must be represented in numbers and figures, and depending on the industry, whether be they for retailers, airlines, hotels, cruise ships, credit cards, investments, utilities, non-profit or business services, variables that would be employed to define seemingly straightforward “high-value customers” would be vastly different. But let’s say that we pick an airline as an example. Let me ask you this; how frequent is frequent enough for anyone to be called a frequent flyer?

Let’s just assume that we are going through an exercise of defining a frequent flyer for an airline company, not for any other travel-related businesses or even travel agencies (that would deal with lots of non-flyers). Granted that we have access to all necessary data, we may consider using:

1. Number of Miles—But for how many years? If we go back too far, shouldn’t we have to examine further if the customer is still active with the airline in question? And what does “active” mean to you?

2. Dollars Spent—Again for how long? In what currency? Converted into U.S. dollars at what point in time?

3. Number of Full-Price Ticket Purchases—OK, do we get to see all the ticket codes that define full price? What about customers who purchased tickets through partners and agencies vs. direct buyers through the airline’s website? Do they share a common coding system?

4. Days Between Travel—What date shall we use? Booking date, payment date or travel date? What time zones should we use for consistency? If UTC/GMT is to be used, how will we know who is booking trips during business hours vs. evening hours in their own time zone?

After a considerable hours of debate, let’s say that we reached the conclusion that all involved parties could live with. Then we find out that the databases from the IT department are all on “event” levels (such as clicks, views, bookings, payments, boarding, redemption, etc.), and we would have to realign and summarize the data—in terms of miles, dollars and trips—on an individual customer level to create a definition of “frequent flyers.”

In other words, we would need to see the data from the customer-centric point of view, just to begin the discussion about frequent flyers, not to mention how to communicate with each customer in the future. Now, it that a job for IT or marketing? Who will put the bell on the cat’s neck? (Hint: Not the dog.) Well, it depends. But this definitely is not a traditional IT function, nor is it a standalone analytical project. It is something in between, requiring a translator.

Customer-Centric Database, Revisited
I have been emphasizing the importance of a customer-centric view throughout this series, and I also shared some details regarding databases designed for marketing functions (refer to: “Cheat Sheet: Is Your Database Marketing Ready?”). But allow me to reiterate this point.

In the age of abundant and ubiquitous data, omnichannel marketing communication—optimized based on customers’ past transaction history, product preferences, and demographic and behavioral personas—should be an effortless routine. The reality is far from it for many organizations, as it is very common that much of the vital information is locked in silos without being properly consolidated or governed by a standard set of business rules. It is not that creating such a marketing-oriented database (or data-mart) is solely the IT department’s responsibility, but having a dedicated information source for efficient personalization should be an organizational priority in modern days.

Most databases nowadays are optimized for data collection, storage and rapid retrieval, and such design in general does not provide a customer-centric view—which is essential for any type of personalized communication via all conceivable channels and devices of the present and future. Using brand-, division-, product-, channel- or device-centric datasets is often the biggest obstacle in the journey to an optimal customer experience, as those describe events and transactions, not individuals. Further, bits and pieces of information must be transformed into answers to questions through advanced analytics, including statistical models.

In short, all analytical efforts must be geared toward meeting business objectives, and databases must be optimized for analytics (refer to “Chicken or the Egg? Data or Analytics?”). Unfortunately, the situation is completely reversed in many organizations, where analytical maneuvering is limited due to inadequate source data, and decision-making processes are dictated by limitations of available analytics. Visible symptoms of such cases are, to list a few, elongated project cycle time, decreasing response rates, ineffective customer communication, saturation of data sources due to overexposure, and—as I was emphasizing in this article—communication breakdown among divisions and team members. I can even go as far as to say that the lack of a properly designed analytical environment is the No. 1 cause of miscommunications between IT and marketing.

Without a doubt, key pieces of data must reside in the centralized data depository—generally governed by IT—for effective marketing. But that is only the beginning and still is just a part of the data collection process. Collected data must be consolidated around the solid definition of a “customer,” and all product-, transaction-, event- and channel-level information should be transformed into descriptors of customers, via data standardization, categorization, transformation and summarization. Then the data may be further enhanced via third-party data acquisition and statistical modeling, using all available data. In other words, raw data must be refined through these steps to be useful in marketing and other customer interactions, online or offline (refer to “‘Big Data’ Is Like Mining Gold for a Watch—Gold Can’t Tell Time“). It does not matter how well the original transaction- or event-level data are stored in the main database without visible errors, or what kind of state-of-the-art communication tool sets a company is equipped with. Trying to use raw data for a near real-time personalization engine is like putting unrefined oil into a high-performance sports car.

This whole data refinement process may sound like a daunting task, but it is not nearly as painful as analytical efforts to derive meanings out of unstructured, unconsolidated and uncategorized data that are scattered all over the organization. A customer-centric marketing database (call it a data-mart if “database” sounds too much like it should solely belong to IT) created with standard business rules and uniform variables sets would, in turn, provide an “analytics-ready” environment, where statistical modeling and other advanced analytics efforts would gain tremendous momentum. In the end, the decision-making process would become much more efficient as analytics would provide answers to questions, not just bits and pieces of fragmented data, to the ultimate beneficiaries of data. And answers to questions do not require an enormous data dictionary, either; fast-acting marketing machines do not have time to look up dictionaries, anyway.

Data Roadmap—Phased Approach
For the effort to build a consolidated marketing data platform that is analytics-ready (hence, marketing-ready), I always recommend a phased approach, as (1) inevitable complexity of a data consolidation project will be contained and managed more efficiently in carefully defined phases, and (2) each phase will require different types of expertise, tool sets and technologies. Nevertheless, the overall project must be managed by an internal champion, along with a group of experts who possess long-term vision and tactical knowledge in both database and analytics technologies. That means this effort must reside above IT and marketing, and it should be seen as a strategic effort for an entire organization. If the company already hired a Chief Data Officer, I would say that this should be one of the top priorities for that position. If not, outsourcing would be a good option, as an impartial decision-maker, who would play a role of a referee, may have to come from the outside.

The following are the major steps:

  1. Formulate Questions: “All of the above” is not a good way to start a complex project. In order to come up with the most effective way to build a centralized data depository, we first need to understand what questions must be answered by it. Too many database projects call for cars that must fly, as well.
  2. Data Inventory: Every organization has more data than it expected, and not all goldmines are in plain sight. All the gatekeepers of existing databases should be interviewed, and any data that could be valuable for customer descriptions or behavioral predictions should be considered, starting with product, transaction, promotion and response data, stemming from all divisions and marketing channels.
  3. Data Hygiene and Standardization: All available data fields should be examined and cleaned up, where some data may be discarded or modified. Free form fields would deserve special attention, as categorization and tagging are one of the key steps to opening up new intelligence.
  4. Customer Definition: Any existing Customer ID systems (such as loyalty program ID, account number, etc.) will be examined. It may be further enhanced with available PII (personally identifiable information), as there could be inconsistencies among different systems, and customers often move their residency or use multiple email addresses, creating duplicate identities. A consistent and reliable Customer ID system becomes the backbone of a customer-centric database.
  5. Data Consolidation: Data from different silos and divisions will be merged together based on the master Customer ID. A customer-centric database begins to take shape here. The database update process should be thoroughly tested, as “incremental” updates are often found to be more difficult than the initial build. The job is simply not done until after a few successful iterations of updates.
  6. Data Transformation: Once a solid Customer ID system is in place, all transaction- and event-level data will be transformed to “descriptors” of individual customers, via summarization by categories and creation of analytical variables. For example, all product information will be aligned for each customer, and transaction data will be converted into personal-level monetary summaries and activities, in both static and time-series formats. Promotion and response history data will go through similar processes, yielding individual-level ROI metrics by channel and time period. This is the single-most critical step in all of this, requiring deep knowledge in business, data and analytics, as the stage is being set for all future analytics and reporting efforts. Due to variety and uniqueness of business goals in different industries, a one-size-fits-all approach will not work, either.
  7. Analytical Projects: Test projects will be selected and the entire process will be done on the new platform. Ad-hoc reporting and complex modeling projects will be conducted, and the results will be graded on timing, accuracy, consistency and user-friendliness. An iterative approach is required, as it is impossible to foresee all possible user requests and related complexities upfront. A database should be treated as a living, breathing organism, not something rigid and inflexible. Marketers will “break-in” the database as they use it more routinely.
  8. Applying the Knowledge: The outcomes of analytical projects will be applied to the entire customer base, and live campaigns will be run based on them. Often, major breakdowns happen at the large-scale deployment stages; especially when dealing with millions of customers and complex mathematical formulae at the same time. A model-ready database will definitely minimize the risk (hence, the term “in-database scoring”), but the process will still require some fine-tuning. To proliferate gained knowledge throughout the organization, some model scores—which pack deep intelligence in small sizes—may be transferred back to the main databases managed by IT. Imagine model scores driving operational decisions—live, on the ground.
  9. Result Analysis: Good marketing intelligence engines must be equipped with feedback mechanisms, effectively closing the “loop” where each iteration of marketing efforts improves its effectiveness with accumulated knowledge on a customer level. It is very unfortunate that many marketers just move through the tracks set up by their predecessors, mainly because existing database environments are not even equipped to link necessary data elements on a customer level. Too many back-end analyses are just event-, offer- or channel-driven, not customer-centric. Can you easily tell which customer is over-, under- or adequately promoted, based on a personal-level promotion-and-response ratio? With a customer-centric view established, you can.

These are just high-level summaries of key steps, and each step should be managed as independent projects within a large-scale initiative with common goals. Some steps may run concurrently to reduce the overall timeline, and tactical knowledge in all required technologies and tool sets is the key for the successful implementation of centralized marketing intelligence.

Who Will Do the Work?
Then, who will be in charge of all this and who will actually do the work? As I mentioned earlier, a job of this magnitude requires a champion, and a CDO may be a good fit. But each of these steps will require different skill sets, so some outsourcing may be inevitable (more on how to pick an outsourcing partner in future articles).

But the case that should not be is the IT team or the analytics team solely dictating the whole process. Creating a central depository of marketing intelligence is something that sits between IT and marketing, and the decisions must be made with business goals in mind, not just limitations and challenges that IT faces. If the CDO or the champion of this type of initiative starts representing IT issues before overall business goals, then the project is doomed from the beginning. Again, it is not about touching the core database of the company, but realigning existing data assets to create new intelligence. Raw data (no matter how clean they are at the collection stage) are like unrefined raw materials to the users. What the decision-makers need are simple answers to their questions, not hundreds of data pieces.

From the user’s point of view, data should be:

  • Easy to understand and use (intuitive to non-mathematicians)
  • Bite-size (i.e., small answers, not mounds of raw data)
  • Useful and effective (consistently accurate)
  • Broad (answers should be available most of time, not just “sometimes”)
  • Readily available (data should be easily accessible via users’ favorite devices/channels)

And getting to this point is the job of a translator who sits in between marketing and IT. Call them data scientists or data strategists, if you like. But they do not belong to just marketing or IT, even though they have to understand both sides really well. Do not be rigid, insisting that all pilots must belong to the Air Force; some pilots do belong to the Navy.

Lastly, let me add this at the risk of sounding like I am siding with technologists. Marketers, please don’t be bad patients. Don’t be that bad patient who shows up at a doctor’s office with a specific prescription, as in “Don’t ask me why, but just give me these pills, now.” I’ve even met an executive who wanted a neural-net model for his business without telling me why. I just said to myself, “Hmm, he must have been to one of those analytics conferences recently.” Then after listening to his “business” issues, I prescribed an entirely different solution package.

So, instead of blurting out requests for pieces of data variables or queries using cool-sounding, semi-technical terms, state the business issues and challenges that you are facing as clearly as possible. IT and analytics specialists will prescribe the right solution for you if they understand the ultimate goals better. Too often, requesters determine the solutions they want without any understanding of underlying technical issues. Don’t forget that the end-users of any technology are only exposed to symptoms, not the causes.

And if Mr. Spock doesn’t seem to understand your issues and keeps saying that your statements are illogical, then call in a translator, even if you have to hire him for just one day. I know this all too well, because after all, this one phrase summarizes my entire career: “A bridge person between the marketing world and the IT world.” Although it ain’t easy to live a life as a marginal person.