Build Customer Happiness Through Retention Marketing and Predictive Analytics

 

You may  know it as Loyalty Marketing, Lifecycle Marketing or Customer Success Marketing, but it all boils down to – keeping customers happy and engaged so they keep coming back. It’s especially important if you’re in an industry where consumers make purchases frequently.

Trying to retain customers is not a new concept. The reason why customers stay loyal is the same today as it was years ago when they shopped at the local corner stores. Grocers developed relationships and customer loyalty by making sure shoppers were happy with their choices, offering products they enjoyed and keeping their customers preferences in mind.

Retention Marketing and it’s goals are still the same; but, the consumer landscape is drastically different. Companies are not just catering to a few folks in a neighborhood, but to thousands and even millions of customers everywhere. The customer shopping journey is also more fragmented than it ever was.  But to earn customer loyalty, companies must still take the effort to know each customer and show them that they are appreciated. On the customer-facing side this means engaging with them at every point in the customer journey, at the right time and with the correct level of enthusiasm.

retentions-now-sliderUntil recently, Retention Marketing to millions of customers was unfeasible for most companies. Only huge companies such as a Walmart, Target or Amazon had the budget for loyalty marketing. But in the last few years, emerging marketing technologies and tools that use Big Data and predictive analytics have made mass personalization possible for a fraction of the cost. Predictive analytics gives companies a competitive edge, but it’s only a matter of time before Retention Marketing through predictive analytics becomes a necessity.

In this article I’ll explain

  • When Retention Marketing is better than Acquisition Marketing
  • The things you need to start Retention Marketing

 

Retention Vs Acquisition

It’s more profitable to retain your customers. Retaining just 5% more high value customers leads to anywhere between 25% to 100% increase in profit per customer according to Frederick F. Reichheld, author of The Loyalty Effect: The Hidden Force Behind Growth, Profits and Lasting Value.  He also showed us that acquiring new customers costs five times as much retaining existing ones.

New customers are expensive because marketers have to raise brand awareness, incentivize consumers to switch from competitors and bear the costs of setting up new accounts, training more sales reps, acclimating customers to your brand, and more. Retaining customers on the other hand is more profitable.

Let’s take a simple example where we sell one product to a new and existing customer:

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In this case, there the profit margins from an existing customer is 125% more!

How do I Start with Retention Marketing?

With the technology that’s available today marketers can learn customer preferences, buying habits, and other interactions to narrow their audiences with ever-increasing specificity. Using this information we could also quantify things like predicted customers lifetime values and level of loyalty that enables marketers to personalize marketing messages.

Collecting Data

I cited an A.T.Kearney report in an earlier post to describe how a majority of shopping journeys in the US are multichannel. And with the expansion of technology like digital wallets, and commerce options for social networks the shopping journey will become even more fragmented.

A customer may try on a product at a store and then immediately purchase it on their phone if the store doesn’t carry it but their website does. Or a customer may browse through reviews and all the different types of cameras on their browser before driving to a store to buy it there and then. It’s important to recognize and collect data on these interactions first before you make them actionable.

Before you can begin segmenting or predicting your customer’s behavior, you need to be able to answer questions like:

  • What’s their gender? Age?
  • What channel is the customer coming from? What did they buy?
  • What was their engagement pattern before a purchase?
  • And many more

You need a 360 Degree Customer Profile before you can even try to understand your customer. I described how AgilOne uses four levels of data to build a true 360 Profile here.

360 Cusotmer ProfileMake sure your customer data is in one place, is usable and accurate. That means no duplicates, data is well integrated and constantly updated. See the steps AgilOne takes to clean your data.

Interpreting the Data

Over several engagements, patterns of preferences and behaviors emerge. Once you have enough data from all the engagement points, integrated and cleaned,  one-on-one marketing becomes possible. Now you can start interrogating your data to find patterns.

However, finding patterns in Big Data sets is not something that can be done manually. Not only, do you have to integrate thousands and millions of data points from hundreds of variables;  you also need to find out how those different variables may be related. Additionally, these patterns need to be checked and updated with new data frequently to get analysis that’s as close to real-time as possible. This not how a marketing team should be spending their time and resources. You would need an entire team of data scientists just to organize and maintain the data, let alone interpret it.

Bonus Video: Our CEO, the Data Chef explains Customer Loyalty

Current Methodology

Most marketers today rely on reporting tools such as OLAP, dashboards, and scorecards to deduce patterns from historical data. Most existing marketing tools put the data through a set of defined hypotheses and present results and KPIs that marketers monitor and analyze. Using their intuition and guesswork, marketers then try to find connections between patterns. This takes time, effort and resources that the marketing team could be using to develop content, run campaigns, or engaging with customers.

Luckily, there are marketing intelligence technologies that not only do this but dramatically improve the process.

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Predictive Analytics

Unlike the marketing tools used by marketers today, predictive analytics doesn’t make any assumptions about the data.  It uses various mathematical modeling techniques and algorithms to look at the data in its entirety to find relationships and patterns that matter, instead of trying to prove or disprove a hypothesis.

Predictive analytic mechanisms are process intensive and become more and more complicated with each data set. Jari Koister, our former VP of Technology, explained some of the clustering mechanisms in greater detail in a few articles that can be found here.

The most common and business applicable way to use predictive analytics is to create predictive models for customers who are most likely to purchase, or find patterns or relationships between types of products bought that a marketer wouldn’t think to look for. Here are 2 simple examples:

Predictive Models

Predictive models are ‘trained’ using historical data. For example, if you wanted to find customers who are most likely to purchase product X,  you would train a model to find the characteristics and behaviors of the customers who’ve also bought product X in past. Comparing a new customer’s characteristics and behaviors to the characteristics and behaviors of customers who’ve purchased product X in the past, AgilOne’s platform can predict the new customer’s likelihood to buy product X.

Clusters

Another predictive technique AgilOne uses is clustering. Unlike predictive models, clustering goes through all the data points to discover optimal correlations that a person wouldn’t have found or looked for. For example, an AgilOne user analyzed their customers shopping habits through the use of machine learning and saw that certain people who bought active wear also buy sunglasses. Of course, additional customers buy sunglasses, but this finding helped this end user target sunglasses towards active people. If you want lo learn more about clustering take a look at these:

Conclusion

New retention platforms and predictive technologies deliver more actionable customer insight than ever before. It shifts the marketing paradigm and provides marketers with more metrics than ever before to really understand customer loyalty. With more data than ever before, marketers can build true 360 degree customer profiles, and create better and better segments. The more defined a segment is, the more qualified the customers are and that’s one of the best ways to measure success.

Here are some other articles to help you get started with retention marketing:

 

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