Exploring Marketing Technology

Category: Data Marketing

Using the power of computing to draw a straighter line between a business and its customers, to the benefit of each

  • What b-to-b customer retention changes would YOU recommend?

    A friend with a successful b-to-b eCommerce business posed a simple question to me: “If you could only do one or two things for an ecommerce business (that sells actual products rather than a service or software or something) to increase customer retention, what would you recommend?” Here are my recommendations, in priority order. What are yours?

    1. Place your web address, with a compelling call-to-action, directly on the products being shipped. Make this call-to-action as time-sensitive as possible. Don’t be lame and do include a deadline. NO: “Fill out our warranty card online.” YES: “Set up an email reminder on our site so you’ll never forget to replenish. Do it by [date] and we’ll give you an automatic 10% off your next purchase, and free shipping!” Enclose a card reiterating the offer. This may be your last best shot at creating a repeat customer.
    2. Follow up your shipment with a “We’d like to know if your products are fitting your needs” email or letter. Include a customer satisfaction survey that rewards them with something they can use with an immediate order. If you’re using snail mail, naturally you should enclose a printed catalog. Draw their attention to related items that can be found within it (or if it’s an email, found on the eCommerce site). If possible make the effort self-financing by generating an immediate re-purchase. Use the Net Promoter Score (NPS)* methodology in the satisfaction survey, to track current loyalty for this customer and as a way to track overall likelihood to repurchase as a trend over time.

    Those are my recommendations. What are yours? Comments are especially welcome, for me and my friend.

    *I’m including this because, although NPS has fallen out of favor as a predictor of company growth, and in other ways is definitely not perfect, I agree with Dale Wolf in that I like its simplicity. You need to use something as a predictive baseline that can (hopefully) be compared with real loyalty measurements. The NPS methodology, associated with Satmetrix Systems, Inc. and Fred Reichheld, is good enough to do the job.

  • What the Netflix prize teaches us about digital teamwork

    A few days ago a team crossed the finish line in a race to develop the best algorithm for the Netflix recommendation engine. It wasn’t easy. It turns out that the type of business logic once carried exclusively between the ears of a good video rental clerk is hard  to automate. Netflix decided they needed help. They placed a price on improving suggestion results: One million dollars for a 10% or better improvement.

    Teams around the world got to work. It took them three years to reach the 10% milestone. And 30 days after one team did, the best results over that threshold took the prize. Here’s the leaderboad.

    We can learn from these teams’ struggles. The leaders who were interviewed all agree they couldn’t have done it without an interdisciplinary approach, tight collaboration and a willingness to be wildly creative. According to a piece in the New York Times, “the formula for success was to bring together people with complementary skills and combine different methods of problem-solving.”

    In the physical world, we know that the more hands you have to lift something, the heavier an object you can lift. But most of us in our digital, information age careers, have a difficult time imagining that this synergy is possible when the heavy lifting is computational. We need to think again.

    Quoted in the Times piece, David Weiss, a member of one of the teams competing, said, “The surprise was that the collaborative approach works so well, that trying all the algorithms, coding them up and putting them together far exceeded our expectations.”

    We’ve seen it work with open source software and multi-player online games. Now we have a very public example that in the digital world as well, many hands make light work.

    Related posts:

  • Netflix understands the strong ROI of improved customer satisfaction

    After three years, the Netflix Prize competition is coming to a triumphant close. This is where the online DVD rental company offered a $1 million reward to anyone who could improve its flawed Cinematch recommendation engine by at least 10 percent. Back when it started, I suggested one novel way that a competing team might improve results (hire a philosopher). We may never know all of the tricks employed by the likely winners.

    And who might these winners be? A little over a week ago, the team called “BellKor’s Pragmatic Chaos” delivered a 10.05% improvement. The Netflix Prize competition has now declared “last call.” The other teams have thirty days to improve on the winning algorithm.

    Two things strike me about this competition. The first is how difficult it is to predict our tastes in films. I’m frankly amazed that anyone is taking the prize. (Remember, teams have been trying for three solid years!)

    The second and more important take-away is this: You can never be content with your present efforts to satisfy customers. They can always be improved — and they should be improved. Even when the cost is surprisingly steep.

  • Is datamining Twitter conversations worth it?

    What started with a piece by David Berkowitz on MediaPost (registration required), on Ten Ways To Decide If Your Business Should Tweet, has turned into an interesting conversation about using Twitter to support a brand, and especially about measuring those efforts. This conversation has been primarily through this lengthy post from earlier today by Marshall Sponder.

    Marshall makes some excellent points (he’s not @WebMetricsGuru for nothing!), including this one: “Social Media isn’t really designed, at this time, to analyze Acquisition or Retention – but Web Analytics, is — and I maintain this is one of the strongest arguments to merge the two, in a formal way, rather than in an informal way.”

    Datamining and CRM

    How do you begin merging these data in a “formal” way? Tools are emerging to allow for the mining of conversations, and linking them where possible to a CRM database. Here’s Marshall’s take on this process:

    David Berkowitz talks about Target Audiences, but you’d first have to figure out what your Target Audience is for your Brand or for a particular product or promotion of your Brand – then do CRM datamining using house database lists, or the Social Media CRM outreach to collect names and classify them according to Target Audience Segmentation — best done with data analytics.   Let’s say, that for the purposes of this post, my article on Entrepreneur.com on Learn to Measure Your Web Presence using Unbound Technology or Rapleaf, is the way to go.

    If you’re a mom-and-pop shop, you’d do nothing as elaborate, more just Twitter research, much as I’ve shown above, but if you’re Zappos, or Dell, well … that’s another story — the story I tell in Learn to Measure Your Web Presence and others, like it.

    Of course, a big brand can make a lot of money whereas the mom and pop shop, probably won’t — so a big brand can afford to spend a lot of money on data mining — and it’s well worth doing because of the potential money and value that can come from it.

    Scarcity of Resources

    The biggest constraint in doing this sort of work isn’t technology. It’s time. Even properly guided, the process takes many people-hours, and that is a resource in short supply for most businesses today. I see a major challenge in the linkage between prospects / customers and Twitter profiles. (Ack!, I can hear you yell. Yet another datapoint to capture in our CRM databases: The client’s Twitter handle!)

    But it is becoming clear that this is an area where a business should focus some of its energies — assuming the business passes David Berkowitz’s Ten Ways test.

    Years ago, Don E. Schultz co-wrote Measuring Brand Communication ROI. In this marketing chestnut, he and his co-authors built a surprisingly relevant model for tracking spending and estimated returns for each brand communication (How old is this book? The included Excel file was loaded on a 5.25″ magnetic diskette). A huge category — and ROI black hole — was customer service.

    Twitter is a communication channel more than a marketing tactic, and this channel has more to do with customer satisfaction and brand education than driving sales. It’s another touchpoint and nothing more.

    But like email and other important touchpoints, it should be measured. Conversations like the one taking place today will help determine how this measurement takes place and to what end.

  • Estimating the true value of a web visitor

    Marketers are still grappling with finding the real value of digital marketing efforts. At the end of a promotional campaign, marketers find even the most “trackable” of web visits difficult to value. If we didn’t know this already, a survey of chief marketing officers, conducted by Atlanta’s Heidrick & Struggles, clarified the problem.

    Kevin Hillstrom, author of Multichannel Forensics, suggests a reasonable way to approach web campaign valuation. It works well for e-commerce site visits, and even sheds light on valuing other types of visitors.

    Placing value on non-buying visitors

    Start the valuation process with the obvious: Visitors who immediately convert to a sale. But then keep going. Apply common sense numbers to those visitors who do not immediately buy. And why not? Visitors may come back two, three or more times before making a purchase. Like a fish that nibbles before biting, these “lost” visits aren’t so lost after all. They should be fairly depreciated, not totally ignored.

    Here’s what Kevin says about ignoring all but the “one-visit” purchasers:

    We try our hardest to allocate orders to the advertising vehicle that caused the order, seldom considering a series of events.

    Take paid search as an example. Assume that a paid search campaign results in a 3% conversion rate and a $100 AOV [Average Order Value]. We run a profit and loss statement on the 0.03 * 100 = $3.00 demand generated by the campaign, factoring in the cost of the campaign.

    What about the 97% of visitors who did not purchase?

    Hillstrom asks, “What if you had this data?”:

    • Of those who are left [i.e., 97% of the base], 50% will visit the website again within one week, with 3% converting, spending $100 each.
    • Of those who are left, 50% will not visit again. Those who are left will visit again within three weeks, with 3% converting, spending $100 each.
    • Of those who are left, 50% will not visit again. Those who are left will visit again within one month, with 3% converting, spending $100 each.
    • Of those who are left, 50% will not visit again. Those who are left will visit again within four months, with 3% converting, spending $100 each.
    • Of those who are left, 50% will not visit again. Those who are left will visit again within six months, with 3% converting, spending $100 each.

    He goes on to explain:

    There is value in each case, value that most of us choose not to measure.
    When I iterate through the five cases above, I calculate an additional $2.75 of future visitor value. [I get $2.68 in my number-crunching, as the number in the lower right of the graphic shows. Here’s my math in a Google Spreadsheet.]

    Value of a Site Visitor Assuming $100 AOVIn other words, we measure the $3.00 generated by short-term conversion. We don’t always measure the $2.75 of future conversions.

    Now there may be additional expenses associated with the $2.75 number — that customer might require additional paid search expense or might use a shopping comparison site, whatever. So we need to run a true profit and loss statement on the additional $2.75 generated by future visits.

    If each first-time visitor (one that doesn’t convert immediately) is worth $0.30 profit over the next twelve months, you think differently about attracting visitors, don’t you?

    I agree. The BrandWeek article I linked to in the first paragraph said that the CMOs surveyed, “Expressed an awareness of digital’s potential, along with a recognition that they weren’t close to tapping it.”

    Building sales models that take into account the messy realities of online behavior is one way we can start.