5 Data-Driven Marketing Catalysts for 2016 Growth

The new year tends to bring renewal, the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable catalysts in their business.

The new year tends to bring renewal and the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable marketing catalysts in their business.

To help you accelerate your thinking, here is a list of those catalysts that have something for everyone, some of which can be great food for thought as you tighten up plans. This year, you will do well if you resolve to do the following five things:

  • Build a Scalable Prospect Database Program. Achieving scale in your business is perhaps the greatest challenge we face as marketers. Those who achieve scale on their watch are the most sought-after marketing pros in their industries — because customer acquisition is far from cheap and competition grows more fiercely as the customer grows more demanding and promiscuous. A scientifically designed “Prospect Database Program” is one of the most effective ways great direct marketers can achieve scale — though not all prospecting databases and solutions are created equally.

A great prospecting database program requires creating a statistical advantage in targeting individuals who don’t already know your brand, or don’t already buy your brand. That advantage is critical if the program is to become cost-effective. Marketers who have engaged in structured prospecting know how challenging it is.

A prospect database program uses data about your very best existing customers: What they bought, when, how much and at what frequency. And it connects that transaction data to oceans of other data about those individuals. That data is then used to test which variables are, in fact, more predictive. They will come back in three categories: Those you might have “guessed” or “known,” those you guessed but proved less predictive than you might have thought, and those that are simply not predictive for your customer.

Repeated culling of that target is done through various statistical methods. What we’re left with is a target where we can begin to predict what the range of response looks like before we start. As the marketer, you can be more aggressive or conservative in the final target definition and have a good sense as to how well it will convert prospects in the target to new customers. This has a powerful effect on your ability to intelligently invest in customer acquisition, and is very effective — when done well — at achieving scale.

  • Methodically ID Your VIPs — and VVIPs to Distinguish Your ‘Gold’ Customers. It doesn’t matter what business you are in. Every business has “Gold” Customers — a surprisingly small percentage of customers that generate up to 80 percent of your revenue and profit.

With a smarter marketing database, you can easily identify these customers who are so crucial to your business. Once you have them, you can develop programs to retain and delight them. Here’s the “trick” though — don’t just personalize the website and emails to them. Don’t give them a nominally better offer. Instead, invest resources that you simply cannot afford to spend on all of your customers. When the level of investment in this special group begins to raise an eyebrow, you know for certain you are distinguishing that group, and wedding them to your brand.

Higher profits come from leveraging this target to retain the best customers, and motivating higher potential customers who aren’t “Gold” Customers yet to move up to higher “status” levels. A smart marketing database can make this actionable. One strategy we use is not only IDing the VIPs, but the VVIP’s (very, very important customers). Think about it, how would you feel being told you’re a “VVIP” by a brand that matters to you? You are now special to the brand — and customers who feel special tend not to shop with many other brands — a phenomenon also known as loyalty. So if you’d like more revenues from more loyal customers, resolve to use your data to ID which customers are worth investing in a more loyal relationship.

  • Target Customers Based on Their Next Most Likely Purchase. What if you knew when your customer was most likely to buy again? To determine the next most likely purchase, an analytics-optimized database is used to determine when customers in each segment usually buy and how often.

Once we have that purchase pattern calculated, we can ID customers who are not buying when the others who have acted (bought) similarly are buying. It is worth noting, there is a more strategic opportunity here to focus on these customers; as when they “miss” a purchase, this is usually because they are spending with a competitor. “Next Most Likely Purchase” models help you to target that spending before it’s “too late.”

The approach requires building a model that is statistically validated and then tested. Once that’s done, we have a capability that is consistently very powerful.

  • Target Customers Based on Their Next Most Likely Product or Category. We can determine the product a customer is most likely to buy “next.” An analytics-ready marketing database (not the same as a CRM or IT warehouse/database) is used to zero-in on the customers who bought a specific product or, more often, in a specific category or subcategory, by segment.

Similar to the “Next Most Likely Purchase” models, these models are used to find “gaps” in what was bought, as like-consumers tend to behave similarly when viewed in large enough numbers. When there is one of these gaps, it’s often because they bought the product from a competitor, or found an acceptable substitute — trading either up or down. When you target based upon what they are likely to buy at the right time, you can materially increase conversion across all consumers in your database.

  • Develop or Improve Your Customer Segmentation. Smart direct marketing database software is required to store all of the information and be able to support queries and actions that it will take to improve segmentation.

This is an important point, as databases tend to be purpose-specific. That is, a CRM database might be well-suited for individual communications and maintaining notes and histories about individual customers, but it’s probably not designed to perform the kind of queries required, or structure your data to do statistical target definition that is needed in effectively acquiring large numbers of new customers.

Successful segmentation must be done in a manner that helps you both understand your existing customers and their behaviors, lifestyles and most basic make up — and be able to help you acquire net-new customers, at scale. Success, of course, comes from creating useful segments, and developing customer marketing strategies for each segment.

‘Big Data’ Is Like Mining Gold for a Watch – Gold Can’t Tell Time

It is often quoted that 2.5 quintillion bytes of data are collected each day. That surely sounds like a big number, considering 1 quintillion bytes (or exabytes, if that sounds fancier) are equal to 1 billion gigabytes. … My phone can hold about 65 gigabytes; which, by the way, means nothing to me. I just know that figure equates to about 6,000 songs, plus all my personal information, with room to spare for hundreds of photos and videos. 

It is often quoted that 2.5 quintillion bytes of data are collected each day. That surely sounds like a big number, considering 1 quintillion bytes (or exabytes, if that sounds fancier) are equal to 1 billion gigabytes. Looking back only about 20 years, I remember my beloved 386-based desktop computer had a hard drive that can barely hold 300 megabytes, which was considered to be quite large in those ancient days. Now, my phone can hold about 65 gigabytes; which, by the way, means nothing to me. I just know that figure equates to about 6,000 songs, plus all my personal information, with room to spare for hundreds of photos and videos. So how do I fathom the size of 2.5 quintillion bytes? I don’t. I give up. I’d rather count the number stars in the universe. And I have been in the database business for more than 25 years.

But I don’t feel bad about that. If a pile of data requires a computer to process it, then it is already too “big” for our brains. In the age of “Big Data,” size matters, but emphasizing the size element is missing the point. People want to understand the data in their own terms and want to use them in decision-making processes. Throwing the raw data around to people without math or computing skills is like galleries handing out paint and brushes to people who want paintings on the wall. Worse yet, continuing to point out how “big” the Big Data world is to them is like quoting the number of rice grains on this planet in front of a hungry man, when he doesn’t even care how many grains are in one bowl. He just wants to eat a bowl of “cooked” rice, and right this moment.

To be a successful data player, one must be the master of the following three steps:

  • Collection;
  • Refinement; and
  • Delivery.

Collection and storage are obviously important in the age of Big Data. However, that in itself shouldn’t be the goal. I hear lots of bragging about how much data can be collected and stored, and how fast the data can be retrieved.

Great, you can retrieve any transaction detail going back 20 years in less than 0.5 seconds. Congratulations. But can you now tell me whom are more likely to be loyal customers for the next five years, with annual spending potential of more than $250? Or who is more likely to quit using the service in next 60 days? Who is more likely to be on a cruise ship leaving the dock on the East Coast heading for Europe between Thanksgiving and Christmas, with onboard spending potential greater than $300? Who is more likely to respond to emails with free shipping offers? Where should I open my next store selling fancy children’s products? What do my customers look like, and where do they go between 6 and 9 p.m.?

Answers to these types of questions do not come from the raw data, but they should be derived from the data through the data refinement process. And that is the hard part. Asking the right questions, expressing the goals in a mathematical format, throwing out data that don’t fit the question, merging data from a diverse array of sources, summarizing the data into meaningful levels, filling in the blanks (there will be plenty—even these days), and running statistical models to come up with scores that look like an answer to the question are all parts of the data refinement process. It is a lot like manufacturing gold watches, where mining gold is just an important first step. But a piece of gold won’t tell you what time it is.

The final step is to deliver that answer—which, by now, should be in a user-friendly format—to the user at the right time in the right format. Often, lots of data-related products only emphasize this part, as it is the most intimate one to the users. After all, it provides an illusion that the user is in total control, being able to touch the data so nicely displayed on the screen. Such tool sets may produce impressive-looking reports and dazzling graphics. But, lest we forget, they are only representations of the data refinement processes. In addition, no tool set will ever do the thinking part for anyone. I’ve seen so many missed opportunities where decision-makers invested obscene amounts of money in fancy tool sets, believing they will conduct all the logical and data refinement work for them, automatically. That is like believing that purchasing the top of the line Fender Stratocaster will guarantee that you will play like Eric Clapton in the near future. Yes, the tool sets are important as delivery mechanisms of refined data, but none of them replace the refinement part. Doing so would be like skipping guitar practice after spending $3,000 on a guitar.

Big Data business should be about providing answers to questions. It should be about humans who are the subjects of data collection and, in turn, the ultimate beneficiaries of information. It’s not about IT or tool sets that come and go like hit songs. But it should be about inserting advanced use of data into everyday decision-making processes by all kinds of people, not just the ones with statistics degrees. The goal of data players must be to make it simple—not bigger and more complex.

I boldly predict that missing these points will make “Big Data” a dirty word in the next three years. Emphasizing the size element alone will lead to unbalanced investments, which will then lead to disappointing results with not much to show for them in this cruel age of ROI. That is a sure way to kill the buzz. Not that I am that fond of the expression “Big Data”; though, I admit, one benefit has been that I don’t have to explain what I do for living for 10 minutes any more. Nonetheless, all the Big Data folks may need an exit plan if we are indeed heading for the days when it will be yet another disappointing buzzword. So let’s do this one right, and start thinking about refining the data first and foremost.

Collection and storage are just so last year.

Olympics Advertisers Fail to Go for the Gold

During the 2008 Olympics, advertisers in both the U.S. and U.K. largely failed to use paid search marketing to promote themselves online after their national teams’ won gold medals.

This bold statement came to me from Steak, a digital marketing and search agency headquartered in New York and London.

Between them, the U.S. and U.K. won 55 gold medals at the 2008 Olympic Games, finishing second and fourth, respectively, in that category behind leader China.

During the 2008 Olympics, advertisers in both the U.S. and U.K. largely failed to use paid search marketing to promote themselves online after their national teams’ won gold medals.

This bold statement came to me from Steak, a digital marketing and search agency headquartered in New York and London.

Between them, the U.S. and U.K. won 55 gold medals at the 2008 Olympic Games, finishing second and fourth, respectively, in that category behind leader China.

In the release, Steak said “analysis of search traffic showed significant spikes in interest in athletes following their gold medal win, signaling an opportunity for sponsors, news organizations and other advertisers to connect with interested consumers.

But Steak’s research shows that few seized the opportunity to use paid search to capitalize on a positive association with the Olympic stars. Search ads showed up against just 35 percent of the U.S. and U.K. gold medal winners’ names. Among others, advertisers who sponsor medalists in particular missed out on some of the highest-profile moments, Steak said.

Steak noted that search interest in U.S. swimmer Michael Phelps, who won a record eight gold medals, skyrocketed between Aug. 10 and Aug. 17, according to its analysis of Google Trends data.

Steak’s research also shows that Phelps’ corporate sponsors, such as Speedo and PureSport performance drinks, started running paid search ads against the swimmer after well after he gained his eight medal.

Similarly, Kerri Walsh and Misty May-Treanor, the popular winners of the women’s beach volleyball gold medal, failed to generate much interest from advertisers. Neither their sponsors nor the AVP or FIVB beach volleyball tours, in which both athletes compete, capitalized on their respective Olympic successes, according to Steak.
Food for thought…