Standing Out In A Crowd(ed) Inbox

During the holiday season, the average consumer receives about 38 promotional emails from retailers each week, according to Chad White, Research Director at Responsys. That’s an average of 5.4 emails a day, all typically arriving between the hours of 7–11am. Now that the holidays are over, it feels like this number has held steady, if not increased. I, for one, wake up each morning and check my smartphone, typically having received a minimum of five new emails from my favorite retailers. Each subject line attempts to outshine the other to get my attention. The onslaught continues when I open my inbox when I get to work. This isn’t surprising because, for retailers, email remains the best marketing channel to get more revenue from their customers: it’s inexpensive, easy to personalize, highly trackable and offers the broadest reach to store, Web and mobile customers. So how can retailers trying to take advantage of this medium stand out in what has become an increasingly crowded inbox?

Get To Know Your Customers (For Real This Time)

This question of how to stand out got me thinking about a panel I had the pleasure of attending while at the NRF BIG Show in New York City. Deb Weinswig from Citigroup interrupted this particular panel (which focused on the benefits of knowing your customers) by saying something along these lines, “I just looked at my personal email, where I get emails from hundreds of retailers, and none of these messages are tailored to me. Not one. And I can’t think of when I ever have gotten one like that.” And she is right. Of the emails I receive each day, I can count on one hand the number of times I have received a message tailored to my personal taste, interests and shopping behavior.

The reality is, the hype about 1:1 marketing and “knowing your customer” got too far ahead of its real capabilities at the time. So when retailers tried new approaches with great expectations, they were either disappointed in the results they saw, shocked by the level of effort it took (particularly on the creative side), or both. Being rational decision makers, retail marketers stopped their personalization efforts, with the exception of a few situations like “triggered” emails where a specific behavior (e.g. abandoning something in your cart) is an immediate trigger for communication. While effective, these triggers are only a small part of the marketing approach. The lion’s share of the email mix is the standard “batch and blast” message to every customer with an email, as often as retailers dare.


Treat Your Customers Like The Individuals That They Are

Knowing your customers, and I mean actually knowing them, is challenging but can be incredibly rewarding. The first thing you need to do is determine what information is relevant and applicable to your email campaigns. The answer for most retailers will be their customers’ shopping behaviors and personal interests and taste. Using the right combination of big data and predictive analytics, retailers have the power to use recent transactions (on- and off-line), reviews, blogs, email logs and other online interactions to decipher a shopper’s individual behaviors and style. Take consumer “Jane”, for example. With the right technology on their side retailers can paint a picture of Jane that looks something like this:

Jane’s Product Affinities

  • Jane loves the latest fashion (4 of 5 items were only sold in that season)
  • “Tight fit”, “skinny” and “chic” are attributes that appear in 3 of 4 apparel of items she purchased
  • 73% of customers who share these characteristics are now buying colored denim and cropped fall jackets
  • Jane likes brands like Gucci and Prada

Jane’s Offer / Price Sensitivity

  • Full price shopper
  • Premium price point in categories

Jane’s Attrition Risk and Momentum

  • Opened 3 of last 7 emails over past 10 days, but did not click through
  • Approaching observed cadence for shoppers like Jane who buy earlier in a new season
  • 62% chance of shopping in next 2 weeks

Jane’s Hole in the Basket Upsell

  • 65% probability of buying a handbag, even though has  never before
  • 73% chance of repeating a purchase in “Dresses”

Put This Knowledge Into Action And Stand Out In A Crowd(Ed) Inbox

So what does the hyper-personalized picture of “Jane” above really mean for a retailer trying to pull together a tailored email campaign? Consider a department store seeking to increase the number of customers buying from a particular product category like handbags. Sending emails featuring new handbag arrivals to previous handbag buyers is easy. But what about customers like Jane who have never bought a handbag? This is when predictive analytics and human judgment come into play.

By capturing data from every relevant customer touch point (like the data listed above,) mining behavioral signals and using advanced predictive algorithms, retailers can determine that Jane has a 65% probability of buying a handbag from said department store. Using that information, the retailer can then tailor an email for Jane based on her past purchases of brands like Gucci and Prada, her sensitivities to price point, the frequency at which she tends to shop and her personal style, to inspire her to click. This methodology has proven to increase total revenue per customer by 10% to 20%, increase click-thru rates 50% to 100% and multiply ROI 10 times.

Email isn’t going anywhere and with new channels for delivery like the tablet and smartphone rapidly gaining popularity, retailers will continue to flock to this medium. But it’s the retailers that harness the power of hyper-personalized customer analytics that will stand out and gain their desired consumer interaction and those that continue to “batch and blast” will just be another deleted subject line.

Rama Ramakrishnan is CEO & Founder of CQuotient. Ramakrishnan is an analytics entrepreneur with more than 20 years of experience in applying analytics to business problems across a range of industries. He founded CQuotient to transform retailer economics by infusing customer insight systematically into their everyday decisions. Prior to CQuotient, Ramakrishnan taught analytics in the MBA program at MIT Sloan School of Management. Earlier in his career, as chief scientist and VP of R&D at price optimization software firm ProfitLogic, Ramakrishnan pioneered the development of techniques for optimally pricing and promoting seasonal and fashion-sensitive merchandise for retailers.

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