How Do We Leverage Data to Drive a Faster Economic Recovery?

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. However, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. And despite the global hardships that will be felt by many, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

When used well, data science will help direct scarce resources to the right opportunities and efficiently drive growth. I am convinced this will be a big differentiator versus previous recoveries of this magnitude.

Over my career, I have consistently encountered inefficient and counter-productive practices in data-driven decision management and have written about them often. They are paralleled in the crisis today. Below are three issues I would like us all to think about when we leverage data science to rebuild the national and world economy.

1. Customer Data Hoarding

Companies collect so much data that many are “drowning in data.” If you have no idea of the value of what you are collecting, then it is digital garbage.

We were led to believe that AI and data mining would help make sense of the data. It does to some extent, but more often it leads to head-scratching conclusions. We can’t leverage what we can’t understand.

As a data-driven consultant, I am amazed at how much time is spent sifting through data just trying to make sense of it all before any valuable insights can be generated. Going forward we cannot afford this luxury. If there are 10 gallons of fuel in the tank, we can’t spend five gallons trying to figure out if the engine works. However, when it comes to mining company data, we often do.

2. It’s About Qualitative, Not Just Quantitative

We can’t be slaves to the data we have. Collecting the right data is often cheap and easily done, if time is taken to plan. This means that measurement strategy can’t be a retrospective exercise. Too often, I have engaged clients in the post-mortem analysis of very important projects. In many cases, my team is often limited to the data that is available and not the data that was needed. Critical answers are sometimes left unanswered. This is a waste of time, resources and most importantly, valuable information.

3. Data Is Not the Solution, It’s the Tool

We must regularly remind ourselves that data does not solve problems or create opportunities. Rather, brave decision making solves problems and creates opportunities. Data is a valuable tool that can only inform the decisions we need to make. It can help lower the risk and provide valuable insights. Sometimes, collecting more data before acting can be wise. Other times it can also be the delay in action that leads to disaster.

What is happening today has no parallel in recent memory. While the 1918 flu pandemic had similar infection rates, the world was a different place then. Today, we have advanced tools and technology to aid our recovery.

Data science will be one of those important tools, especially if we collectively decide to use it to its true potential. As a result, I am hopeful that we can come out of this faster than we realize.

Data Mining: Where to Dig First?

The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators between the business world and the technology world. This in-between position often creates a great amount of confusion for aspiring data scientists.

Data mining
“Big_Data_Prob,” Creative Commons license. | Credit: Flickr by KamiPhuc

In the age of abundant data, obtaining insights out of mounds of data often becomes overwhelming even for seasoned analysts. In the data-mining business, more than half of the struggle is about determining “where to dig first.”

The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators between the business world and the technology world. This in-between position often creates a great amount of confusion for aspiring data scientists, as the gaps between business challenges and the elements that makes up the answers are very wide, even with all of the toolsets that are supposedly “easy to use.” That’s because insights do not come out of the toolsets automatically.

Business questions are often very high-level or even obscure. Such as:

  • Let’s try this new feature with the “best” customers
  • How do we improve customer “experience”?
  • We did lots of marketing campaigns; what worked?

When someone mentions “best” customers, statistically trained analysts jump into the mode of “Yeah! Let’s build some models!” If you are holding a hammer, everything may look like nails. But we are not supposed to build models just because we can. Why should we build a model and, if we do, whom are we going after? What does that word “best” mean to you?

Breaking that word down in mathematically representable terms is indeed the first step for the analyst (along with the decision-makers). That’s because “best” can mean lots of different things.

If the users of the information are in the retail business, in a classical sense, it could mean:

  • Frequently Visiting Customers: Expressed in terms of “Number of transactions past 12 months,” “Life-to-date number of transactions,” “Average days between transactions,” “Number of Web visits,” etc.
  • Big Spenders: Expressed in terms of “Average amount per transaction,” “Average amount per customer for past four years,” “Lifetime total amount,” etc.
  • Recent Customers: Expressed in terms of “Days or weeks since last transaction.”

I am sure most young analysts would want requesters to express these terms like I did using actual variable names, but translating these terms into expressions that machines can understand is indeed their job. Also, even when these terms are agreed upon, exactly how high is high enough to be called the “best”? Top 10 percent? Top 100,000 customers? In terms of what, exactly? Cut-out based on some arbitrary dollar amount, like $10,000 per year? Just dollars, or frequency on top of it, too?

The word “best” may mean multiple things to different people at the same time. Some marketers — who may be running some loyalty program — may only care for the frequency factor, with a hint of customer value as a secondary measure.

But if we dig further, she may express the value of a customer in terms of “Number of points per customer,” instead of just dollar spending. Digging even deeper, we may even have to consider ratios between accumulated points vs. points redeemed over a certain period to define what “best” means. Now we are talking about three-dimensional matrix — spending level, points earned, and points redeemed — just to figure out what the best segment is. And we didn’t even begin to talk about the ideal size of such target segment.

Understanding long- and short-term business goals, and having “blends” of these figures is the most important step in data mining. Again, knowing where to dig is the first step.

Let’s take another example. If we introduce the “continuity” element in all of this — like in telecommunication, subscription or the travel businesses — the word “best” takes yet another different turn. Now we have to think about the longevity of the relationship, in addition to transaction and loyalty elements. For example:

  • Tenure: Expressed in terms of “Years since member signup,” “Months since first transaction,” or “Number of active months since signup”
  • Engagements: “Number of contacts for customer service, trouble-shooting, complaints, or package changes/upgrades”
  • Other Activities: Such as cancelation, delinquent payment, move or reactivation

For the airline business, “best” may mean different things for each flight. Data elements to consider could be:

  • Mileage program status
  • Lifetime mileage/YTD mileage
  • Ticket class/code
  • Ticket price paid for the flight/Discount amount
  • Frequency of the flight (Number of flights in the past 12 months, average days between flights/bookings)
  • Peripheral purchases and paid upgrades

Why do I list all of these tedious details? Because analysts must be ready for any type of business challenges and situations that decision-makers may throw at them.

Another example would be that even in the same credit card company, depending on the division — such as acquisition team and CRM team — the word “best” may mean completely different things. Yes, they all care for “good” customers, but the acquisition team may put more weight on responsiveness, while the CRM team may care for profitability above all else.

Speaking of customer care, “customer experience” can be broken down into multiple variables, again to pose different options to decision-makers. What is the customer experience made of, and what do we need to understand about the whole customer journey? In the age where we collect every click, every word and every view, defining such parameters is very important to get to the answers out fast.

In the sea of data, basically we need to extract the following elements of “experience”:

  • The Subject Matter or Product in Question: Why is the customer contacting us? Start with issue classifications and related product and product category designations. If they are in free form, better get them tagged and categorized. Difficulty level of the issue resolution can be assigned, as well.
  • Number of Actions and Reactions: Expressed in terms of number of contacts/inbound calls per customer, number of outbound calls, chats or services visits per customer.
  • Resolution: In no obscure terms, what was the outcome? Resolved or not resolved? Satisfactory or unsatisfactory? If they are embedded in some call log, better employ text analytics, pronto.
  • How Long Did All of This Take? Expressed in terms of “Minutes between initial contact and resolution,” “Average minutes between actions,” “Average duration of engagements,” etc. Basically, the shorter the better for all of this.

Good customer experience, this way, can be measured more objectively. Reporting required for evaluation of different scenarios can be improved immensely when the building blocks (i.e., variables and metrics) are solid.

Now let’s move onto yet another common question of “what worked — or didn’t work — in various marketing efforts.” Consultants often encounter this type of question, and the biggest hurdle often isn’t the analytics process itself, but messy, disparate, and unstructured data. To understand what worked, well, we must define what that means. First off, what was the desired outcome?

  • Opens and Clicks: Traditional digital analytics metrics
  • Conversion: Now we need to dig into transaction data and attribute them to proper campaigns and channels
  • Renewal: If it is for B-to-B or continuity programs
  • Elevation of Brand Image: Tricky and subjective, so we would need to break down this obscure word, as well.

As for what marketers did to invoke responses from customers or prospects, let’s start breaking down that “what” of the “What worked?” question from that angle. Specifically:

  • Channel: A must-have in the omnichannel world.
  • Source: Where the contact name came from?
  • Selection Criteria: How did you choose the name to contact? By what variable? If advanced analytics were employed, with what segment, what model and what model groups?
  • Campaign Type/Name/Purpose: Such as annual product push, back-to-school sale, Christmas offer, spring clearance, etc.
  • Product: What was the main product featured in the campaign?
  • Offer: What was the hook? Dollar or percentage off? Free shipping? Buy-one-get-one-free? No-payment-until? Discount for a limited period?
  • Creative Elements: Such as content version, types of pictures, font type/size, tag lines, other graphic elements.
  • Drop Day/Time: Daypart of the campaign drop, day of the week, seasonal, etc.
  • Wave: If the campaign involved multiple waves.
  • A/B Testing Elements: A/B testing may have been done in a more controlled environment, but it may be prudent to carry any A/B testing elements on a customer level throughout.

These are, of course, just some of the suggestions. Different businesses may call for vastly different sets of parameters. I tell analysts not to insist on any particular element, but to try to obtain as much clean and dirty data as possible. Nonetheless, I am pointing out that breaking the elements down this way, upfront, is a necessary first step toward answering the “what worked” question.

I have been saying “Big data must get smaller” (refer to “Big Data Must Get Smaller”) for some time now. To do that, we must define the question first. Then we can narrow down the types of data elements that are necessary to (1) define the question in a way that a machine can understand, and (2) derive answers in more comprehensive and consistent ways.

True insights, often, are not a simple summary of findings out of fancy graphical charts. In fact, knowing where to dig next is indeed a valuable insight in itself, like in mining valuable minerals and gems. Understanding where to start the data mining process ultimately determines the quality of all subsequent analytics and insights.

So, when faced with an obscene amount of data and ambiguous questions, start breaking things down to smaller and more tangible elements. Even marketers without analytical training will understand data better that way.

An ABC Introduction to Data Mining for Dollars: Slicing and Dicing Your In-House List for Profit (Part 2 of 2)

In my last post, I introduced the RFM method, an effective direct response strategy to slice and dice your list for better conversion rates. The “R” represented recency—how long your customers have been with you. Today, I’m going to talk about the other components of frequency and monetary.

In my last post, I introduced the RFM method, an effective direct response strategy to slice and dice your list for better conversion rates.

The “R” represented recency—how long your customers have been with you.

Today, I’m going to talk about the other components of frequency and monetary:

Frequency
This segmentation tactic is another way to break down your house list: by how frequently customers have bought from you. So once you’ve divided your list based on recency, you look at it in terms of your customers’ purchase behavior. First, you identify your multi-buyers—customers who’ve purchased more than one product from you. You then split this list further, segmenting out two-time, three-time, four-time (and more) buyers. Those who have bought from you most often have proven their loyalty and obviously like the products and services they’ve been getting from you.

So if, for example, you’re considering launching a new product with a high price point, these would be your best prospects.

Monetary
Finally, you look at your list in terms of money. One way to do this is to divide your list by the amount of money each customer has spent with you. You might, for example, assign a benchmark dollar amount, such as $5,000, $10,000 or more. Customers at that level make up your “premium buyers.” This is the group that has the most favorable LTV for your company. These are your “VIPs.” Once you discover who your VIPs are, you can design products or offers specifically for them. Let’s say you have some kind of exclusive—and expensive—lifetime membership club. You would market this to multi-buyers who also fall into your “premium buyer” category.

If you offer payment options to your customers, another monetary way to divide your list is according to the payment options they have chosen: monthly, quarterly, yearly, etc. This will help you determine the initial purchase tolerance of each group of customers and which ones may respond best to future price points. As you can see, by looking at your customers’ purchasing habits—recency, frequency and monetary—you can identify the best customers for certain products. And by offering a product to customers who are likely to want it, you can improve your conversion rates.

By using the proven RFM method and other data-mining techniques, I’ve seen conversion rates double and triple. I’ve also seen inactive subscribers’ open rates surge from 0 percent to more than 30 percent.

However, many companies that send emails don’t have the capacity for data mining.
Unfortunately, some smaller businesses or start-up companies typically cut robust email features and analytics for cost savings. Oftentimes, these companies save money using online email service providers that can certainly get the job done, but don’t offer segmentation tools that allow for list analysis, where you can dissect your database into subgroups or “buckets.”

So when you’re searching for an email service provider, try to project what your segmentation needs may be going forward and if data mining is a strategy that you’ll want to deploy.

Hot Tip! When looking at email marketing companies, make sure you ask if there’s a list segmentation or data mining feature that can easily be done through their email platform. Find out the level of segmentation capacity (how far the segmentation of data can be drilled down to); if certain segmentation features are a standard feature or an upgrade; and what those costs may be on a monthly basis. Sometimes it may be an additional fee, but will certainly pay for itself over time.

An ABC Introduction to Data Mining for Dollars: Slicing and Dicing Your In-House List for Profit (Part 1 of 2)

One of the best ways to build your online business is to build your list; that is, your “database” of potential subscribers, customers or prospects. This may not be as sexy as social marketing, as robust as mobile marketing or as challenging as search engine marketing … but it is a viable way to harness the power within your own “house file” to maximize your marketing ROI.

One of the best ways to build your online business is to build your list; that is, your “database” of potential subscribers, customers or prospects. This may not be as sexy as social marketing, as robust as mobile marketing or as challenging as search engine marketing … but it is a viable way to harness the power within your own “house file” to maximize your marketing ROI.

Today, I’ll show you how you can segment your database of names to boost sales, increase bonding and shorten conversion time. Data mining, list segmentation or strategic database marketing is basically the art of slicing and dicing your own in-house list of names for optimal performance. You do this to help increase the response of your promotional and conversion efforts.

You see, once you divide your list of names into smaller groups (known as segmentation), you can target your product offers and promotional messages to each of those groups. By customizing your marketing messages based on specific customer needs, you’ll be promoting products to people who are more likely to buy them. You increase your customers’ satisfaction rate as well as your potential conversion rates. And higher conversion rates mean more money for your company.

One data-mining model is the RFM method. It’s practiced by direct response marketers all over the world. “R” stands for Recency—how recently a customer has made a purchase. “F” stands for Frequency—how often the customer makes a purchase. And “M” stands for Monetary—how much the customer spends. Here’s how you can use the RFM method to help lift your sales.

Recency
Whether your house list is made up of people who signed up to receive your free e-zine or people who paid for a subscription, you can segment your database according to how long your subscribers have been with you. For instance, you can create categories such as: 0-6 months, 6-12 months, and 12-plus months. You would look at these groups as your hot subs (newest subscribers 0-3 months), warm subs (mid-point subscribers) and cool subs (those who have been subscribing to your e-zine the longest, 12-plus months).

Here’s one way you can put that data to use …

Let’s say some of your “cool subs” have lost their initial enthusiasm for your e-zine. You could cross-reference those names with their open rates. If most of these subscribers haven’t been opening your e-zine in six, nine or 12 months, you may consider sending them a special message asking to reengage them. These “inactive” subscribers are a great group on which to test new marketing approaches, new prices and new subject lines. Since this group is not responding to your current emails, why not use this as a platform to reengage AND test? Your “hot subs” are your newest, most enthusiastic subscribers. They are ripe to learn more about you, your products and your services. If you handle this group properly, you can cultivate them into cross-sell and up-sell customers.

For example, send your “hot subs” a special introductory series of emails (also known as auto responder series). This special series would encourage bonding and introduce readers to your e-zine’s contributors and overall philosophy. It could also tempt readers with specially priced offers. Sending an introductory series like this can not only increase the number of subscribers who convert to paying customers, it also increases their lifetime value (LTV)—the amount they spend with you over their lifetime as your customer. Hot Tip! Make sure to suppress the recipients of your auto responders from any promotional efforts until the series is complete to ensure more effective bonding.

If, instead of subscribers to a free e-zine, your house list is made up of people who paid for their subscription, the same segmentation process applies. You break your active subscribers into hot subs, warm subs and cool subs. You also break out “expires” (those who allowed their subscription to run out) and “cancels” (those who cancelled their subscription).

Cross-marketing to these lists is usually effective. The expires oftentimes simply forget to renew and need a reminder. And just because someone cancelled one subscription doesn’t mean they may not be ideal for another service or product that you provide. If they’re still willing to receive email messages from you, add these folks to your promotional lists. Once you’ve gotten these cancelled subscribes to open your messages, turning them into paying customers is just a matter of time. Most Internet marketers would have written these people off. So any revenue you get from them is ancillary.

Next time, I’ll go into Frequency and Monetary, the two other components of the RFM model. So stay tuned!