Don’t be Left Behind When it Comes to Predictive Marketing

August 28, 2018

Don’t be left behind when it comes to predictive marketing-497824-edited

Predictive marketing is a new, data-driven marketing paradigm. It is a way to approach marketing, with the customer at the center. Companies using predictive marketing focus on developing and managing customer relationships rather than just developing and selling products.

Customers are demanding more meaningful relationships with brands

We as consumers are bombarded with marketing and frankly are fed up. Retail research agency Conlumino conducted a consumer survey in late 2014, that showed many consumers have come to expect some form of personalization - in part because the larger and more established brands have been serving up personalized experiences for some years now.

By asking more than 3,000 adult online shoppers about what information they expected companies to know about them and what personalized experiences they appreciate, the survey uncovered that over 70 percent of shoppers want brands to deliver some type of personalized experience, whether it is an alert about a new product that matches their interests, a refill reminder, or VIP customer recognition.

The findings suggest that a deep understanding of your customers and hyper-targeting is crucial to effective customer engagement and building brand loyalty:

  • More than half of consumers in the US and UK expected e-commerce sites to remember their past purchases
  • Among US shoppers, the most popular personalized experiences were emails offering discounts on products they previously viewed (66%), alerts when products they like are on sale (57%) and VIP customer appreciation rewards (51%)
  • About half of Americans want to receive a new customer welcome greeting, versus only 34% in the UK
  • Shoppers, aged 18 to 34, part of the “millennial” generation, were more likely to appreciate almost all forms personalization: 52% of “millennials” expect brands to remember their birthday as compared to 21% of those aged 65+

The customers of Traeger Grills, a manufacturer of barbeque grills and smokers, were demanding more relevancy. They didn’t write or call Traeger to tell them this, but rather the company started to experience a rising number of opt outs when sending emails. Clearly customers were saying that the one-size-fits-all email campaigns were not suiting their needs. Today Traeger customers receive much more relevant and timely emails, such as replenishment reminders to reorder pellets at just the right time. Using predictive marketing has increased the click through rate of their email campaigns by 300% and increased the take rate from 1% to 4%.

Marketers need to change their thinking dramatically. We believe that all customers deserve to be served relevant and respectful communications. Instead of sending one hundred messages with relevancy one, marketers should start to think about sending one message with relevancy one hundred.

Early adopters show that predictive marketing delivers enormous value

McKinsey estimates that optimizing marketing using predictive analytics technologies represents a $200 billion global opportunity for firms.

Large companies like Netflix and Amazon have been using predictive analytics for years. The row of movies and TV shows “you might like” that appear when you curl up on the couch and turn on Netflix is a driving force of the company’s success and is all made possible by the translation of customer data with smart analytics. : “75% of what people watch [on Netflix] is from some sort of recommendation”, said Netflix’s Research Director Xavier Amatriain on the company’s tech blog.

An increase in revenues of 10% to 100% is not unusual when using predictive marketing and individual campaign gains can be even greater. Harrah’s Entertainment has gone on record to state that using predictive analytics sales increased by 68%, for Sainsbury revenues increased 12%, the publishing company Meredith saw take rates on email campaigns increase by 50% and Earthlink reduced churn by 30%, all using predictive analytics.

New technologies are available to make predictive marketing easy

Although it has been around for decades, predictive analytics is a technology whose time has 
finally come. In marketing, predictive analytics can deliver on the promise of hyper-targeted customer relationship marketing. If you have been in marketing for a while, you might know how difficult it is to extract customer data from your IT department. Customer data is certainly there but often requires SQL queries and a lot of patience to extract and analyze. Fortunately easy to use and affordable technologies have recently become available to put customer data and predictive analytics directly in the hands of marketers.

A variety of market forces have joined to make this possible, including recent advances in big data processing, cloud computing and the availability of open API’s. Until recently, building your own on-premise customer data warehouse and hiring your own team of data scientists was very expensive. New Software-as-a-Service solutions have become available that significantly reduce these costs.

Case Study: Mavi

A good example of a mid-market company that has achieved significant success with predictive marketing is Mavi, a high fashion clothing manufacturer and retailer based in Istanbul, Turkey known for its organic denim favored by celebrities and supermodels. Mavi operates about 350 multinational stores and sales channels in the U.S., Canada, Australia, Turkey and 10 European countries.

Mavi is an early adopter of predictive marketing. When Mavi first got started, each department, including marketing and IT, used its own set of marketing reports and customer data, including key performance indicators. This led to cumbersome cross-referencing and impeded important decision making. Like many companies, the Mavi marketing team initially didn’t have access to customer data without relying on IT resources. This was the first problem that the team tackled. Mavi deployed a modern, cloud-based predictive marketing solution in 2009. This allowed the company to consolidate, cleanse and de-dupe their customer data on a daily basis. Now they were ready to start using data in hyper-personalized campaigns.

One of the first predictive marketing programs that Mavi tested was a program around specific buying personas. Mavi used predictive analytics to find groups of people with distinct product preferences. In predictive lingo these are called product-based clusters. Mavi found at least three very different groups of shoppers: customers who favored mostly woven shirts, others who favored beachwear, whereas a third persona mostly shopped for new season high fashion and accessories. Mavi started to use these personas to implement more targeted marketing campaigns via email and SMS. Specifically, it implemented a re-engagement campaign for lapsed customers that featured the right types of products and creative with the right customers. Using these clusters, Mavi was able to reactivate 20% of lapsed customers. This was a big breakthrough since every customer saved or reactivated reduces Mavi’s need to acquire new customers.

Mavi today is running over 35 different predictive marketing programs. Collectively, these campaigns helped to increase Mavi revenues by 7% in the first few years - which is a huge sum on a dollars and cents basis.

Elif Oner, Mavi’s head of customer relationship management recommends all marketers to get started with predictive marketing. She says: “Start small and pick just one program and build on that success.” Elif is also the CFO’s most favorite marketer because every dollar she spends in marketing, every discount she gives, is accounted for, tested and optimized.