Measuring customer loyalty can be a tricky proposition for some marketers, but, according to Tom Salutz, CEO of DataCo Solutions, these metrics must include a sense of value attached to a particular consumer.
Loyalty360 caught up with Salutz to discuss this theme and others related to customer data.
What performance metrics should brands consider when assessing customer loyalty?
Salutz: Every business should have a sense of the value of a customer. Assessing loyalty program tactics demands a value statistic that represents the actual life of a customer with a business. In general, most customers have a life of two to five years, but this varies as much by business as it does by the products, marketing, and promotional efforts.
Our approach is to match the analysis and metrics to the behavior, products, and marketing tactics. As an example, a company whose product is purchased one time or rarely, we will look at shorter term performance metrics, things like Net Promoter Score, ticket size, etc.
Conversely, a company whose product is purchased repeatedly or over a regular time period, we will look at a longer term, time-based performance metrics. Things like time between purchases, time since last purchase, recency of purchase, etc.
How would you describe the state of customer data in the loyalty marketing world today?
Salutz: Data sources, in the past, were extremely limited. In general, the data was third-party demographics and sales transactions. As segmentation capabilities and CRM systems began to evolve, the data assets began to increase; unfortunately, companies didn’t know what to do with the newly found and expanding data and systems.
Today, even companies who do not value data are collecting it, either inadvertently from operational systems, or as part of standard maintenance with web/social media sources.
What is being done well, in regards to data, and where do the challenges lie?
Salutz: Operational software keeps getting better and cheaper. It is now viable for small and medium-sized businesses to purchase off-the-shelf systems and use them with minimal or no customization. These systems capture and store data as a matter of course, offering new possibilities for data mining. The learning and insights available, even with a cursory analysis are now more easily attained than ever before.
Challenges? While the technology is getting better, the big challenge lies with operational software and the data that result from its utilization. The software does not lend itself to research and analysis, specifically, the element of time or data over a specific time period. Because these systems are operational in nature, they are built to tell you how your world looks right now, introducing time, past or future, is simply not a capability designed into these systems. A great example is Salesforce/SAP/HR systems; we work a lot with these systems and the data they provide. A large portion of our engagements that involve these systems is allocated to reconstructing historical results for use in predicting future trends, insights and outcomes.
How do you see social media (and its expanse of data) being used differently today than a year ago, and how do you see it changing over the next year?
Salutz: As mentioned above, as the channels expand, so does the data. The impact today is that the data available is quickly outpacing the ability to effectively analyze it for insight and meaning.
A year ago attribution was a hot topic. As the channels and media utilized and the data tracked continue to multiply, attribution will be more than a topic. It will become an obsession of both CMOs and CFOs.
What new media do you see your clients using to drive customer loyalty? What would you, as a data scientist, like to see your clients try?
Salutz: Media should mirror your customers’ choices, from Snapchat to Twitter. The one media element that can be counted on into the future is mobile. We see our clients adapting, but struggling to keep up with this ever-evolving customer engagement channel.
As a data scientist, I would like to see Loyalty join forces with Customer Experience. There is no escaping the fact that customer loyalty is largely determined by the customer’s experience.
There has been some great work recently under the heading “shopper insights.” Even if you are not selling products inside a physical store, the same techniques can be applied to website interactions and person-to-person sales.
What are the biggest challenge and/or opportunity you see in mobile as it relates to the capture, analysis, and actionability of data?
Salutz: Mobile data has great potential for analysis and insights. The challenge and/or opportunity is to treat mobile data collection the same as consumer research. It should be very clear what data is being collected and for what purpose. Consumers on research panels receive validation from being listened to; transitioning this same feeling to a mobile research base could provide a great amount of data and active feedback. The key to action ability is blending application design, research design, and panel management.
What trends are you seeing in Loyalty / Customer Loyalty / Marketing as a result of Big Data and Predictive Analytics?
Salutz: This may surprise you, but with the exception of some core business applications, such as recommendation engines and the like, most applications developed from big data have marginal effects on a business.
What we find is that the larger a business and the bigger the data it has, the impact it can make to its business is marginalized by the higher cost to get to the data, the complexity of understanding the data, and the barriers that size creates to implementation.
Not surprisingly, we are seeing the most progress being made correlating and influencing customer behavior via data and predictive analytics, with our mid- to smaller-sized clients.