5 Tips for Successful o2o Channel Leaping

The most strategically planned offline direct marketing effort can be sabotaged by weak links in an online sales order processing system. Moving a prospect from any offline channel marketing to online ordering has its clear benefits, but can be tricky. Whether from direct mail, broadcast, or other print source, your offline to online (o2o) channel redirection must be carefully designed, tested, and refined to maximize the conversion process. So here are five recommendations to ensure a seamless o2o leap.

The most strategically planned offline direct marketing effort can be sabotaged by weak links in an online sales order processing system. Moving a prospect from any offline channel marketing to online ordering has its clear benefits, but can be tricky. Whether from direct mail, broadcast, or other print source, your offline to online (o2o) channel redirection must be carefully designed, tested, and refined to maximize the conversion process. So here are five recommendations to ensure a seamless o2o leap.

In a past era, we direct marketers pitched our offer to our lists. When the prospect decided to buy, they would use a reply envelope to mail or phone their response. While that still happens today, more and more direct marketers prefer to drive a prospect to the web.

There is often a disconnect between concept and execution of taking a prospect from offline to online. We’re so close to the process that we sometimes assume a seamless o2o flow, but while fumbling around a keyboard, the prospect’s attention can be diverted. The online order experience can be clunky or even confusing. Sometimes too much is asked on the online order screen, and information overload sets in. Or we assume the customer is tech-savvy when in fact, they’re not. Orders and carts are abandoned because the prospect gives up.

What to do to ensure a seamless o2o leap? Here are five recommendations:

  1. Clarity Rules: Create a detailed flow chart of every possible path a prospect could take before they press “buy” to see if there is any unanswered or confusing language or visuals. Ensure that there are no dead-ends, and allow them to back up. And, be sure the form they’re returning to is still populated with their original entries, rather than being shown an infuriating screen full of blank fields.
  2. Roadmap the Journey: Manage expectations for your prospect with an overview of the process, why it’ll be worth their time, and how easy and quick it will be, especially if placing an order has multiple options.
  3. Wireframe to Visualize: If you, the marketer, are having trouble visualizing how it all works, just imagine how confused your customer will be. Developing even a crude wireframe will help ensure you don’t overlook something, or that the process unfolds logically and obviously.
  4. Clear Copy: Write to the reading level of your audience, but remember that online channels tend to be one where people are more rushed and scanning. They don’t always read for detail. Make it clear and simple.
  5. Tell and Sell with Video: People may not read copy as closely online, but they are apt to invest time watching a video with tips on how to place their order. It can save the customer time, and help reduce abandoned carts.

The back-end programming of online order systems are usually someone else’s responsibility. But, if you’re the marketer or copywriter, you need to put serious thought and effort into the customer-facing side, so it’s clear, friendly, and quick. Your prospect forms a lasting impression of your entire organization when you have an o2o channel leap requirement. And, if it’s muddled or worse, you may never have another opportunity to make it positive.

Beyond RFM Data

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

How so? At the risk of sounding like a pompous mathematical smartypants (I’m really not), it is because people do not change that much, or if so, not so rapidly. Every move you make is on some predictive curve. What you been buying, clicking, browsing, smelling or coveting somehow leads to the next move. Well, not all the time. (Maybe you just like to “look” at pretty shoes?) But with enough data, we can calculate the probability with some confidence that you would be an outdoors type, or a golfer, or a relaxing type on a cruise ship, or a risk-averse investor, or a wine enthusiast, or into fashion, or a passionate gardener, or a sci-fi geek, or a professional wrestling fan. Beyond affinity scores listed here, we can predict future value of each customer or prospect and possible attrition points, as well. And behind all those predictive models (and I have seen countless algorithms), the leading predictors are mostly transaction data, if you are lucky enough to get your hands on them. In the age of ubiquitous data and at the dawn of the “Internet of Things,” more marketers will be in that lucky group if they are diligent about data collection and refinement. Yes, in the near future, even a refrigerator will be able to order groceries, but don’t forget that only the collection mechanism will be different there. We still have to collect, refine and analyze the transaction data.

Last month, I talked about three major types of data (refer to “Big Data Must Get Smaller“), which are:
1. Descriptive Data
2. Behavioral Data (mostly Transaction Data)
3. Attitudinal Data.

If you gain access to all three elements with decent coverage, you will have tremendous predictive power when it comes to human behaviors. Unfortunately, it is really difficult to accumulate attitudinal data on a large scale with individual-level details (i.e., knowing who’s behind all those sentiments). Behavioral data, mostly in forms of transaction data, are also not easy to collect and maintain (non-transaction behavioral data are even bigger and harder to handle), but I’d say it is definitely worth the effort, as most of what we call Big Data fall under this category. Conversely, one can just purchase descriptive data, which are what we generally call demographic or firmographic data, from data compilers or brokers. The sellers (there are many) will even do the data-append processing for you and they may also throw in a few free profile reports with it.

Now, when we start talking about the transaction data, many marketers will respond “Oh, you mean RFM data?” Well, that is not completely off-base, because “Recency, Frequency and Monetary” data certainly occupy important positions in the family of transaction data. But they hardly are the whole thing, and the term is misused as frequently as “Big Data.” Transaction data are so much more than simple RFM variables.

RFM Data Is Just a Good Start
The term RFM should be used more as a checklist for marketers, not as design guidelines—or limitations in many cases—for data professionals. How recently did this particular customer purchase our product, and how frequently did she do that and how much money did she spend with us? Answering these questions is a good start, but stopping there would seriously limit the potential of transaction data. Further, this line of questioning would lead the interrogation efforts to simple “filtering,” as in: “Select all customers who purchased anything with a price tag over $100 more than once in past 12 months.” Many data users may think that this query is somewhat complex, but it really is just a one-dimensional view of the universe. And unfortunately, no customer is one-dimensional. And this query is just one slice of truth from the marketer’s point of view, not the customer’s. If you want to get really deep, the view must be “buyer-centric,” not product-, channel-, division-, seller- or company-centric. And the database structure should reflect that view (refer to “It’s All About Ranking,” where the concept of “Analytical Sandbox” is introduced).

Transaction data by definition describe the transactions, not the buyers. If you would like to describe a buyer or if you are trying to predict the buyer’s future behavior, you need to convert the transaction data into “descriptors of the buyers” first. What is the difference? It is the same data looked at through a different window—front vs. side window—but the effect is huge.

Even if we think about just one simple transaction with one item, instead of describing the shopping basket as “transaction happened on July 3, 2014, containing the Coldplay’s latest CD ‘Ghost Stories’ priced at $11.88,” a buyer-centric description would read: “A recent CD buyer in Rock genre with an average spending level in the music category under $20.” The trick is to describe the buyer, not the product or the transaction. If that customer has many orders and items in his purchase history (let’s say he downloaded a few songs to his portable devices, as well), the description of the buyer would become much richer. If you collect all of his past purchase history, it gets even more colorful, as in: “A recent music CD or MP3 buyer in rock, classical and jazz genres with 24-month purchase totaling to 13 orders containing 16 items with total spending valued in $100-$150 range and $11 average order size.” Of course you would store all this using many different variables (such as genre indicators, number of orders, number of items, total dollars spent during the past 24 months, average order amount and number of weeks since last purchase in the music category, etc.). But the point is that the story would come out this way when you change the perspective.

Creating a Buyer-Centric Portrait
The whole process of creating a buyer-centric portrait starts with data summarization (or de-normalization). A typical structure of the table (or database) that needs to capture every transaction detail, such as transaction date and amount, would require an entry for every transaction, and the database designers call it the “normal” state. As I explained in my previous article (“Ranking is the key”), if you would like to rank in terms of customer value, the data record must be on a customer level, as well. If you are ranking households or companies, you would then need to summarize the data on those levels, too.

Now, this summarization (or de-normalization) is not a process of eliminating duplicate entries of names, as you wouldn’t want to throw away any transaction details. If there are multiple orders per person, what is the total number of orders? What is the total amount of spending on an individual level? What would be average spending level per transaction, or per year? If you are allowed to have only one line of entry per person, how would you summarize the purchase dates, as you cannot just add them up? In that case, you can start with the first and last transaction date of each customer. Now, when you have the first and last transaction date for every customer, what would be the tenure of each customer and what would be the number of days since the last purchase? How many days, on average, are there in between orders then? Yes, all these figures are related to basic RFM metrics, but they are far more colorful this way.

The attached exhibit displays a very simple example of a before and after picture of such summarization process. On the left-hand side, there resides a typical order table containing customer ID, order number, order date and transaction amount. If a customer has multiple orders in a given period, an equal number of lines are required to record the transaction details. In real life, other order level information, such as payment method (very predictive, by the way), tax amount, discount or coupon amount and, if applicable, shipping amount would be on this table, as well.

On the right-hand side of the chart, you will find there is only one line per customer. As I mentioned in my previous columns, establishing consistent and accurate customer ID cannot be neglected—for this reason alone. How would you rely on the summary data if one person may have multiple IDs? The customer may have moved to a new address, or shopped from multiple stores or sites, or there could have been errors in data collections. Relying on email address is a big no-no, as we all carry many email addresses. That is why the first step of building a functional marketing database is to go through the data hygiene and consolidation process. (There are many data processing vendors and software packages for it.) Once a persistent customer (or individual) ID system is in place, you can add up the numbers to create customer-level statistics, such as total orders, total dollars, and first and last order dates, as you see in the chart.

Remember R, F, M, P and C
The real fun begins when you combine these numeric summary figures with product, channel and other important categorical variables. Because product (or service) and channel are the most distinctive dividers of customer behaviors, let’s just add P and C to the famous RFM (remember, we are using RFM just as a checklist here), and call it R, F, M, P and C.

Product (rather, product category) is an important separator, as people often show completely different spending behavior for different types of products. For example, you can send me fancy-shmancy fashion catalogs all you want, but I won’t look at it with an intention of purchase, as most men will look at the models and not what they are wearing. So my active purchase history in the sports, home electronics or music categories won’t mean anything in the fashion category. In other words, those so-called “hotline” names should be treated differently for different categories.

Channel information is also important, as there are active online buyers who would never buy certain items, such as apparel or home furnishing products, without physically touching them first. For example, even in the same categories, I would buy guitar strings or golf balls online. But I would not purchase a guitar or a driver without trying them out first. Now, when I say channel, I mean the channel that the customer used to make the purchase, not the channel through which the marketer chose to communicate with him. Channel information should be treated as a two-way street, as no marketer “owns” a customer through a particular channel (refer to “The Future of Online is Offline“).

As an exercise, let’s go back to the basic RFM data and create some actual variables. For “each” customer, we can start with basic RFM measures, as exhibited in the chart:

· Number of Transactions
· Total Dollar Amount
· Number of Days (or Weeks) since the Last Transaction
· Number of Days (or Weeks) since the First Transaction

Notice that the days are counted from today’s point of view (practically the day the database is updated), as the actual date’s significance changes as time goes by (e.g., a day in February would feel different when looked back on from April vs. November). “Recency” is a relative concept; therefore, we should relativize the time measurements to express it.

From these basic figures, we can derive other related variables, such as:

· Average Dollar Amount per Customer
· Average Dollar Amount per Transaction
· Average Dollar Amount per Year
· Lifetime Highest Amount per Item
· Lifetime Lowest Amount per Transaction
· Average Number of Days Between Transactions
· Etc., etc…

Now, imagine you have all these measurements by channels, such as retail, Web, catalog, phone or mail-in, and separately by product categories. If you imagine a gigantic spreadsheet, the summarized table would have fewer numbers of rows, but a seemingly endless number of columns. I will discuss categorical and non-numeric variables in future articles. But for this exercise, let’s just imagine having these sets of variables for all major product categories. The result is that the recency factor now becomes more like “Weeks since Last Online Order”—not just any order. Frequency measurements would be more like “Number of Transactions in Dietary Supplement Category”—not just for any product. Monetary values can be expressed in “Average Spending Level in Outdoor Sports Category through Online Channel”—not just the customer’s average dollar amount, in general.

Why stop there? We may slice and dice the data by offer type, customer status, payment method or time intervals (e.g., lifetime, 24-month, 48-months, etc.) as well. I am not saying that all the RFM variables should be cut out this way, but having “Number of Transaction by Payment Method,” for example, could be very revealing about the customer, as everybody uses multiple payment methods, while some may never use a debit card for a large purchase, for example. All these little measurements become building blocks in predictive modeling. Now, too many variables can also be troublesome. And knowing the balance (i.e., knowing where to stop) comes from the experience and preliminary analysis. That is when experts and analysts should be consulted for this type of uniform variable creation. Nevertheless, the point is that RFM variables are not just three simple measures that happen be a part of the larger transaction data menu. And we didn’t even touch non-transaction based behavioral elements, such as clicks, views, miles or minutes.

The Time Factor
So, if such data summarization is so useful for analytics and modeling, should we always include everything that has been collected since the inception of the database? The answer is yes and no. Sorry for being cryptic here, but it really depends on what your product is all about; how the buyers would relate to it; and what you, as a marketer, are trying to achieve. As for going back forever, there is a danger in that kind of data hoarding, as “Life-to-Date” data always favors tenured customers over new customers who have a relatively short history. In reality, many new customers may have more potential in terms of value than a tenured customer with lots of transaction records from a long time ago, but with no recent activity. That is why we need to create a level playing field in terms of time limit.

If a “Life-to-Date” summary is not ideal for predictive analytics, then where should you place the cutoff line? If you are selling cars or home furnishing products, we may need to look at a 4- to 5-year history. If your products are consumables with relatively short purchase cycles, then a 1-year examination would be enough. If your product is seasonal in nature—like gardening, vacation or heavily holiday-related items, then you may have to look at a minimum of two consecutive years of history to capture seasonal patterns. If you have mixed seasonality or longevity of products (e.g., selling golf balls and golf clubs sets through the same store or site), then you may have to summarize the data with multiple timelines, where the above metrics would be separated by 12 months, 24 months, 48 months, etc. If you have lifetime value models or any time-series models in the plan, then you may have to break the timeline down even more finely. Again, this is where you may need professional guidance, but marketers’ input is equally important.

Analytical Sandbox
Lastly, who should be doing all of this data summary work? I talked about the concept of the “Analytical Sandbox,” where all types of data conversion, hygiene, transformation, categorization and summarization are done in a consistent manner, and analytical activities, such as sampling, profiling, modeling and scoring are done with proper toolsets like SAS, R or SPSS (refer to “It’s All About Ranking“). The short and final answer is this: Do not leave that to analysts or statisticians. They are the main players in that playground, not the architects or developers of it. If you are serious about employing analytics for your business, plan to build the Analytical Sandbox along with the team of analysts.

My goal as a database designer has always been serving the analysts and statisticians with “model-ready” datasets on silver platters. My promise to them has been that the modelers would spend no time fixing the data. Instead, they would be spending their valuable time thinking about the targets and statistical methodologies to fulfill the marketing goals. After all, answers that we seek come out of those mighty—but often elusive—algorithms, and the algorithms are made of data variables. So, in the interest of getting the proper answers fast, we must build lots of building blocks first. And no, simple RFM variables won’t cut it.

When Mistakes Happen

Mistakes are a part of the learning process. Every company will experience them at one time or another. Ideally, with good planning, they will be minor and won’t happen often. With better planning, there is an action plan in place to quickly right the wrong. Knowing what to do before it needs to be done simplifies fixing the problem.

My Coke Rewards Apology Email
This My Coke Rewards apology email was delivered quickly and followed the four best practices of making amends for a marketing mistake.

Mistakes are a part of the learning process. Every company will experience them at one time or another. Ideally, with good planning, they will be minor and won’t happen often. With better planning, there is an action plan in place to quickly right the wrong. Knowing what to do before it needs to be done simplifies fixing the problem.

Handling mistakes well is a great loyalty builder. You can measure the effect by conducting a comparative analysis. Pull two segments to compare from customers who made their first purchase five years ago. Choose customers who are very similar in order source, size and selection. Select people who had seemingly perfect orders for the first segment. “Perfect orders” describe orders that are processed quickly and delivered without issues. Place people who had problems quickly resolved for the second segment.

Detail sales history, average order and returns for each segment. Use the information to compare the value of the customers who had problems with the ones who didn’t. This analysis almost always finds that the people who had problems quickly resolved are much more valuable than those who had a perfect order. I believe there is a simple explanation for this: People who have problems resolved to their satisfaction trust the company more. Trust and loyalty go hand in hand.

Planning for failure seems counterintuitive, but it is the best way to be prepared. The first part of the action plan is determining the extent of the problem. Will an apology suffice, or does something need correcting? Apologies are sufficient when the mistake is simple and doesn’t overly inconvenience the person or create an expense.

My Coke Rewards provides us with a good example of a mistake where an apology is enough. Last month, the automated points’ expiration notice malfunctioned. Members received a notification that they needed to add or use points or they would expire. The deadline for keeping the account active was two weeks before the email was sent. The apology came quickly and followed best practices (refer to the image in the media player):

  • Be direct with the apology and explanation.
  • Tell people what they need to do (if anything).
  • Thank them for their business.
  • If necessary, offer a reward for the inconvenience. (If you offer a reward in the form of a discount, make it dollars off with no minimum. This is a payment for a mistake, not a marketing promotion.)

The email from My Coke Rewards was simple, to the point and didn’t offer compensation. The mistake was minor, so an apology after the correction was enough. Bigger mistakes require more. There isn’t a magic formula that determines the ideal response for every problem. Customers are individuals with unique expectations.

The second part of the action plan is determining the specific resolution for each problem. Creating a general list of potential problems and resolutions provides a guide for the customer service team. Anything that satisfies the customer and falls within the guidelines should be resolved immediately.

The best way to determine what needs to be done is to ask the customer with the problem. Lead with an apology and follow with the inquiry. For example: “I’m sorry this happened. What can we do to make it right?” There will occasionally be an outlandish demand, but usually the requested solution is less than you were prepared to do. Asking customers how to right a wrong simultaneously gives them respect and shows that you care. Here are some other best practices when a mistake happens:

  • Minimize customers’ investment in resolving issues. Strive to resolve issues on the first contact without involving other people whenever possible.
  • If you discover the mistake before the customer, reach out immediately. This shows your customers that you are watching their backs.
  • Use the appropriate communication tool. Email works well for most correspondence as long as the messages are not from “do not reply” boxes.
  • When the resolution process is complete, ask customers if they are satisfied with the solution. Every customer cannot be saved, but letting them go without trying is unacceptable.
  • Avoid fake apologies. Apologizing works so well in relationship building that people are making up reasons to do it. Don’t.

Email Marketing Redefined: Service With a Side of Sales

The multichannel marketplace has blurred the line between service and sales. People expect to get answers to their questions while they are shopping and on-demand after an order is placed. Redirecting them to another channel or platform for pre-sale and post-order information has a negative effect on the buying experience and long-term loyalty

The multichannel marketplace has blurred the line between service and sales. People expect to get answers to their questions while they are shopping and on-demand after an order is placed. Redirecting them to another channel or platform for pre-sale and post-order information has a negative effect on the buying experience and long-term loyalty.

Unfortunately, technology has changed faster than the corporate organizational chart has adapted. Marketing and operational departments aren’t integrated enough to provide the seamless shopping and service experience that people want. It’s time to make the shift to integrated messaging across all channels, platforms and departments. The email program is the best place to start, because changes are quick and easy.

Transactional emails tend to be matter of fact announcements of order receipt, shipment and issues. They serve the operational side of the business well but do little to directly improve sales. Branding is minimal and the messages are rarely in the same voice used for promotional information. Failure to include marketing service messages is a lost opportunity.

Marketing is a service when it solves people’s problems. Transactional emails are one to one communication. The right combination of marketing and service messages benefit customers by helping them maximize the return from their investments. The key to successful execution is having the correct processes, careful planning, and good application of business rules. When done well, they keep customers informed and motivate them to buy more.

For example, the order confirmation email should thank the customer for the business, provide specific purchase information, and suggest other items that complement the original products.

An email for an order of earrings could offer a matching necklace or an order for a vacuum cleaner might suggest bags and filters. If the operational process allows combining the orders at the same shipping rate, the suggestion to do so creates a sense of urgency. The only catch is that business rules have to be accurate with personal messaging to optimize the return.

Inserting product images with a brief description will bump sales a bit, but it doesn’t have the same effect as: “Thank you for your order of the super suction vacuum cleaner. It will ship tomorrow. Please remember that the filter needs to be changed every month. Add one on to your order by clicking this link before midnight tonight and there will not be an extra shipping charge.” Of course your copy team will do a better job than me, but you get the idea.

Almost every transactional email sent to customers should include a marketing message. The exceptions to this rule are issue-related emails. Following “your item is out of stock until next month” with “buy this to go with your item” won’t win customer loyalty.

To get started with integrated marketing and service emails:

  1. Review your transactional emails. When are they sent? What information do they include? Is there a follow-up after the sale to encourage people to provide feedback? Do you ask people if they like their purchases? Document all of the transactional emails so you will have a starting point.
  2. Identify opportunities for marketing messages. Add-on sales are good for order confirmation emails. “New items just arrived” works well on shipment confirmation messages. Be creative when thinking about how to combine service and sales, it will provide more testing options.
  3. Select the emails and messages to test. Start small and learn quickly. Testing provides the best information for rolling out your program. Use simple business rules and build from that foundation. Complicated processes are recipes for disaster when you are starting an integrated program.
  4. Verify that the offers are deliverable. Promising your customers that you will combine orders when it is operationally impossible creates mistrust with customers and colleagues. Always under promise and over deliver. It surprises customers and minimizes dissatisfaction.
  5. Measure everything. What effect does the new messaging have on sales? Opens? Clicks? Lifetime value? Lifespan? The more you know the better you can create targeted emails that deliver sales and satisfaction.
  6. Revise as needed. Transactional emails are easy to set and forget. They continue to go out day after day without any maintenance required. This tends to make them a low priority. Scheduling regular updates to rework the emails keep them fresh and informative for customers. It optimizes the return.

Email Marketing Redefined: The 3 Keys to Customer Retention

Memorable experiences make people more likely to return when they need your products or services again. Memories are made by both good and bad experiences. You expect customers to place another order after a good experience. Yet, surprisingly, they are more likely to return after a bad experience when the issues are resolved than after an uneventful good experience. Solving the problems that contribute to a bad experience creates trust, and the more people trust your company, the more they buy

The best customer retention strategies begin with the first order and continue until the lifespan is complete. Everything that happens from the first visit to completion of the final order is part of the experience of shopping with your company. Memorable experiences make people more likely to return when they need your products or services again. Memories are made by both good and bad experiences.

You expect customers to return to place another order after a good experience. Yet, surprisingly, customers are more likely to return after a bad experience when the issues are resolved than after an uneventful good experience. Solving the problems that contribute to a bad experience creates trust, and the more customers trust your company, the more they buy.

Consistently keeping promises also builds trust. When an order is placed, fulfillment is expected. Simply fulfilling orders will not retain customers because every legitimate business fulfills orders. You have to do more to differentiate your company from the competition. Relationships retain customers. Email allows companies in high-volume business to communicate with people on a one-to-one basis. This establishes relationships. Yes, it is at a superficial level, but it serves the purpose of personalizing the customer experience and significantly improves retention rates.

Most people aren’t fooled into thinking that “[insert name here]” emails are personal. They know that is a form letter, but that doesn’t matter as long as the information included is relevant. People placing orders are not looking for best friends, they are looking to solve a problem with minimal effort. The problem may be not having the perfect outfit for the next party, the best coffee maker, a service that would make their jobs easier, or a variety of other challenges. Whatever the problem, if your company provides the solution, keeps the customer informed, and makes everything as easy as possible, people will keep coming back for more.

There are three key components to an effective customer retention strategy:

  1. Knowledge of the Customer Lifecycle—Knowing how people normally act provides insight into drop-off points and inspires ideas for keeping them from leaving. When you know how each segment of your customer base typically performs, you can recognize when someone prematurely drops out of the buying cycle.
  2. Execution of a Detailed Communication Plan—Good communication is the key to all successful relationships. Sharing information about order processing, special sales, use of products and available services contributes to customer retention because it simplifies the buying and consumption process.
  3. An Automated Reactivation Process—Reactivation must start as soon as a customer reaches the first drop-off point. When you know your customer types well enough to know when they have passed the next order point without making a purchase, you can catch them before they are completely gone. Email automation simplifies the reactivation process. Create a strategy designed to connect with customers before they migrate to a competitor.

Plan your reactivation strategy to start while people are still in the active buying cycle. Every email sent from your business to your customers should have a retention element in it, such as these:

  • Make people feel valued and appreciated
  • Solve problems before people ask for help
  • Provide value above and beyond offering low prices
  • Keep people informed throughout the buying process
  • Provide information on the use of products and services
  • Create a bond between company and customer

Is Your Customer Service Killing Customer Loyalty?

As marketers, we spend a lot of time, money, energy and brain power designing and building programs that will drive inquiries, close sales or increase brand engagement. And once a sale is secured, we move into loyalty mode—lovingly nurturing that customer to buy more and buy more often in order to derive a long term revenue stream and ROI for the marketing investment. … So what the hell is wrong with the customer service folks?

As marketers, we spend a lot of time, money, energy and brain power designing and building programs that will drive inquiries, close sales or increase brand engagement.

And once a sale is secured, we move into loyalty mode, lovingly nurturing that customer to buy more and buy more often in order to derive a long term revenue stream and ROI for the marketing investment.

So what the hell is wrong with the customer service folks?

Didn’t they get the memo that says, “Our customers are those people who make sure you get your paycheck. So let’s treat them with respect, concern and understanding. Because if we do, they’ll keep buying from us again, and again and again.”?

Apparently, the customer service folks at Dell never got the memo—and shame on them, because they’ve now lost my business for life.

Granted, I run a smaller agency and my lack of future purchases will not put Dell out of business. But I think there’s a big lesson that many companies can learn from my experience, and that’s to take a moment to really examine what goes on inside these departments.

For the record, we’ve been purchasing Dell products for well over 10 years now. Laptops, towers, printers, screens … you name it. My IT guy likes the ease of ordering online and the ability to carefully customize each of our purchases for the user.

So when we recently did a little expansion by hiring a new employee, we turned once again to Dell for a new desktop PC. Little did we know it would be the last transaction we’d ever make with them—and all because of how we were treated when something went wrong with the order. Here’s a quick factual summary:

  • Friday, Aug. 24: Order placed online.
  • Monday, Aug. 27: Order ships.
  • Tuesday, Sept. 4: According to the FedEx tracking number, the order was delivered and signed for—unfortunately, FedEx delivered it to the wrong company at the wrong address!
  • Thursday, Sept. 6: FedEx reroutes package to us. It arrives and appears to have been opened and resealed. Since this is a PC, I don’t want an opened box, so we try to refuse the delivery. FedEx persists and requires us to contact their customer service to arrange a return to sender.
  • Monday, Sept 10: FedEx picks up tower.
  • Monday, Sept 10: Alert Dell; they promise to “expedite” a replacement order.
  • Friday, Sept 14: Dell informs us the PC is still “being built.”

I must interrupt the facts to say “Wha–?” When we ordered the first time, it took them 2 days to build it. But when we ordered our replacement, it’s now taking more than 5 days to build the same computer? It only gets better …

  • Monday, Sept. 17: Dells says, “Still building.”

What on earth are they building for us? We try to reach a “customer care” rep. (BTW, I HATE that term. I wish organizations would call a spade a spade— it’s plain old customer service. Or perhaps since “service” doesn’t seem to be part of the equation, that’s why they changed it. So they “care” but they cannot “service”?)

Net-net, phone numbers we are provided don’t work. (Ring, ring, ring… apparently Dell hasn’t heard of that new-fangled technology called voicemail.) Emails go unanswered, emails to the supervisor bounce back as “out of the office.” Did I mention my new employee is twiddling thumbs doing idle work as she can only get so much done on her smart phone?

  • Tuesday, Sept. 18: Dell emails us saying the order will now be “escalated” and we’ll be kept aware of the status.

Okay Dell. It’s been 25 days since I placed my order and there is still no confirmed delivery date is sight. I give up. I cancel the order and buy from a local retailer.

No apologies from Dell to try and retain my business. No offers on a future purchase. Nothing. Nada. Apparently Dell’s customer care folks forgot that those marketing millions spent on driving in leads, nurturing relationships and transacting sales have all been an investment in their job security.

Not only did Dell blow it, but I won’t even attempt to make another purchase from them—ever.

As a customer, I get infuriated just thinking about this incident. As a marketer, I cringe.

If you are responsible for marketing in your organization, do you spend any time at all investigating what goes on in “customer care”? You should—because it may be the reason you’re not making your marketing and sales goals.

Introducing ‘The Integrated Email’ Blog by Debra Ellis

Why is email marketing so effective? Is it the one-to-one communication, ability to connect with customers and prospects on the go, or the provision of instant gratification with one-click shopping? The answer depends on the company and the customer relationship, but there is one universal truth: The combination of interactive communication with self-service solutions makes email the most versatile tool in a marketing workshop.

Why is email marketing so effective? Is it the one-to-one communication, ability to connect with customers and prospects on the go, or the provision of instant gratification with one-click shopping? The answer depends on the company and the customer relationship, but there is one universal truth: The combination of interactive communication with self-service solutions makes email the most versatile tool in a marketing workshop.

My experience with email marketing began shortly after Hotmail launched the first Web-based email service in 1996. A client had compiled approximately 11,000 customer email addresses and wondered what we could do with them. Our first test was a 25 percent discount on any order placed that day. A text-only message was sent using the mail merge functionality in Excel and Outlook. It took over two hours to send all the emails.

Those two hours were quite exciting. We had two computers in close proximity so we could watch the progress of the outgoing emails and monitor sales on the website. Within minutes of starting the email transmissions, orders started flowing in. By the end of the day, more than 1900 orders were received. A handful of people asked to be excluded from future mailings. Over 200 people responded with personal notes. Some were grateful for the discount. Others apologized for not placing an order and asked to receive more emails.

Things are much different today. The novelty of receiving a personalized message from a company is long gone. Spam filters make getting emails delivered a near impossible mission. And the competition for recipients’ attention is at an all-time high. Even so, email marketing remains one of most effective marketing and service vehicles available.

The emails that deliver the best return on investment are the ones that are integrated with the other marketing channels to provide information and service to the recipients. They create a connection between company and customer that motivates people to respond. A successful email marketing strategy builds loyalty while increasing sales.

Many email campaigns today are little more than a systematic generation of one promotional email after another. Discount emails are relatively easy to create and deliver sales with each send, making them a quick way to inject some life into lagging sales. The simplicity of sale marketing combined with solid response rates creates an environment where marketers are reluctant to move beyond the easy, low-hanging fruit.

In addition to generating sales, discount marketing also trains people to always look for the best price before buying the company’s products and services. It is not a sustainable strategy because there will always be another company that can offer lower prices and lure customers away. A better plan is to develop an integrated email marketing strategy that educates and encourages people to develop a relationship with the company. This requires more effort, but it delivers loyalty and long-term results.

Every email that a customer or prospect receives is an opportunity for the company to establish itself as the best service provider and solidify the relationship. Best practices include:

  • Using a valid return email address so the recipient can respond with one click.
  • Sending branded emails that identify your company at first glance.
  • Mixing educational emails that provide “how to” information for products and services with new product launches and promotional messages.
  • Transactional emails that communicate shipping information and challenges so customers aren’t left wondering, “Where is my order?”
  • Highly targeted and personalized emails designed to engage customers and prospects at every point in their lifespan.

Finding the right combination of educational, event and promotional emails requires testing and measuring results for incremental improvements. The resources invested improve relationships, increase sales and create a sustainable marketing strategy.

Note: Over the next few months, we’ll feature winning and losing email marketing strategies and campaigns on this blog. If you would like to share your company’s killer emails, send them to Debra at dellis@wilsonellisconsulting.com.

The ‘A’ Word—Learn It, Love It, Live It!

I attended a seminar earlier in January held by the Direct Marketing Club of New York titled “Annual Outlook: What to Expect in Direct & Digital Marketing in 2012.” The main speaker at the event was Bruce Biegel, managing director at the Winterberry Group, a strategic consulting firm that focuses on advertising and marketing.

I attended a seminar earlier in January held by the Direct Marketing Club of New York titled “Annual Outlook: What to Expect in Direct & Digital Marketing in 2012.” The main speaker at the event was Bruce Biegel, managing director at the Winterberry Group, a strategic consulting firm that focuses on advertising and marketing.

For those of you who have never before attended an event where Biegel presents, I highly recommend attending one if you get a chance. He’s a highly engaging speaker with many interesting insights gleaned from years of experience in the field, and backed by the research and analytics of the Winterberry Group.

The focus of the presentation was a review of the marketing and advertising world of 2011, along with some predictions for 2012. According to Biegel, 2011 was the year in which many firms intensified their focus on reporting and analytics tools. For 2012, he predicted many marketers will finally begin to pursue true multichannel integration across their firms, driven by data, analytics and the quest for cross-channel attribution. He touched on the term attribution repeatedly, referring to it as the “Holy Grail” of multichannel marketing.

In a marketing sense, I define attribution—or the “A-word” for the purposes of this blog post—as the act of determining what marketing channel or budget was responsible for generating a particular action: be it a click, lead, order, etc. As a direct marketer, I just love this word. And you should, too. Attribution is where the rubber meets the road. Attribution is what separates the men from the boys, the measurable from the immeasurable, direct response from … well, branding. Not to disparage brand marketing, but I think I can speak for most—if not all—colleagues in the industry when I say that demonstrable attribution is really what has always separated direct response marketing from branding—analytics that essentially give us the ability to calculate the actual ROI of every precious marketing dollar we spend. Enough said.

But, let’s face it, there’s a dirty little secret in the direct response community that those outside of it might not necessarily be aware of. The fact is that attribution has not been all it’s cracked up to be over the past 10 years—and a far cry from an exact science, to say the least. We have the Internet to thank for that. To elaborate, let’s take a moment and turn back the clock around 15 to 20 years, and think back to a time in which the Web did not play such a prominent role in our lives. Back then, most direct response marketing was done via direct mail, catalogs and inserts, as well as DRTV. In this relatively simplistic world, customers could only really place orders using the return mailer or by calling a toll-free number. That was it. Since each piece was stamped with a keycode, attribution was as easy as: “Could you please tell me the five-digit code on the bottom right-hand corner of the order form” … and we knew with certainty why the sale originated.

Then along came the Web—and, with it, an entirely new channel for consumers to interact with their brands. And this is when things got confusing. Let’s say, for example, a consumer received a postcard or catalog from a company. In place of calling the toll-free number, he could instead go to Google and search for the website, find it, locate the products he’s interested in and place an order. Now who gets the credit for the sale? The direct mail team? The search engine marketing team? The catalog team? The email team? All of them? None of them? The fact is, there was really no scientific way to tell for sure. The gears of attribution broke down, creating a vast gray area of uncertainty where the worlds of traditional and new media converged. This was the direct marketer’s dirty little secret in the age of Web 1.0.

To deal with this mess, new techniques and technologies invariably emerged to bring some order to the chaos. Before long, many marketers turned to the concept of campaign-specific landing pages to send their cross-media (or cross-channel) customers to. At least this bypassed the regular website and kept and sales or leads it made in one bucket, separate from the home page and other Web traffic. This was a huge improvement.

Then other technologies like personalized URLs, or PURLS, entered the mix. Gimmicks aside, PURLs work because they are a tool for attribution—not because they give someone a link made out of their name. Sure, giving someone a personalized link is nice … but that’s only window dressing and obfuscates the real value of this cross-media technology. PURLs help marketers attribute activity to the direct mail channel. That’s it in a nutshell. Now of course, there are additional benefits, such as improved Web traffic rates resulting from personalized content, and higher website conversion rates due to a simplified workflow on a landing page that’s been optimized for this purpose alone. But the real value of this technology is attribution—and don’t ever let anyone else tell you otherwise.

Similarly, across other channels useful cross-media technologies emerged like QR Codes, which really solve in mobile the same issue marketers face on desktop Web browsers—namely, the inability to properly track and attribute cross-media actions resulting from their offline campaigns. When push comes to shove, sending individuals to purpose-built, mobile-optimized landing pages, personalized or not, enables precise tracking and measurement, not to mention a better overall user experience and, presumably, a higher conversion rate, too.

Looking forward, the next stage in attribution will most certainly need to deal with the advent of Web 2.0 and the world of social media. Seeing as firms are now making investments in social media strategy, CMOs are going to want to attach some kind of ROI calculation to the mix. Now, of course, you could pretty easily argue that it’s absurd to try to assign any type of ROI to social media in the first place. In that vein, Scott Stratten has a great blog post called “Things We Should Ask The ROI Question About Before Social Media” on UnMarketing that does just that pretty convincingly. But that’s an argument for another time and place. Regardless of whether you feel it’s a smart policy, I think it’s safe to say that where the marketing dollars go, pressure will ultimately follow to show value (ROI).

At the same time, regardless of what dollars are being spent and how these expenditures make CFOs hyperventilate, social media can and do generate sales for organizations. This is an indisputable fact and should not be up for debate anymore. What is in question is the ability of firms to track what happens in social media and attribute the activity to this emerging channel. As we speak, we’re starting to see the introduction of the first generation of effective tools (SocialCRM) that track social media interactions among pools of prospects or leads, and make them available to marketing teams for actionable analysis and follow up. Very cool stuff. But, of course, social media data are only one piece of a much larger puzzle, named “Big Data.” I briefly touched on Big Data in a previous post titled “Deciphering Big Data Is Key to Understanding Buyer’s Journey.”

Actually, on that note, I think this is a good place for me to call it a day. Not only am I running out of space for this post, but that last thought will make a great segue to my next post, which will address the amazing transformation that is taking place within many firms as they deal with the endless volumes of unstructured data (Big Data) they are tracking and storing every day. This wholesale repurposing aims not only to make sense out of this trove of data, but also to break down the walls separating the various silos where the data are stored, such as CRM/SocialCRM platforms, social media websites, marketing automation tools, email software, Web servers and more. Stay tuned next time for more on this topic.

Until then, I welcome any questions, comments or feedback.