Five big mistakes we see with restaurant loyalty program management

These are five big mistakes we see with restaurant loyalty program management, based on in-depth program and data analysis of seventeen restaurant loyalty programs:

  1. Set it and forget it. A program is launched and put on auto-pilot.  Nobody’s steering.
  2. Count what’s easy. How many members do we have?  Instead of – How are members behaving?
  3. Assume incorrectly that all the members in your program have visited at least once.
  4. Rely on and trust averages when it comes to member visits and spending.
  5. Presume that the members who were active last year will also be active this year.

If some of these apply to you, it might be time to step back and take a fresh look at things:

  1. DON’T set it and forget it. The following guidelines will help you ensure that you’re not in ‘set it and forget it mode’.
  2. DON’T count what’s easy. Counting the number of members is a very basic starting point, but you should monitor more meaningful KPIs on a regular, periodic basis.  Overall and by store location, these should include new members added, cumulative members, members active in a period, average visits per active member and average check.
  3. DON’T assume all your members have visited at least once. Look at your enrolled/registered members to see how many have not had a visit.  That’s fertile ground for specific campaigns and offers to get these members off the sidelines and into the game.  Do this periodically as more new members come on board without a visit.
  4. DON’T rely on and trust averages when it comes to member visits and spending. I’ve seen companies look at primitive averages such as: our average member visits 5.7 times per year and spends $161.60.  The average guest simply does not exist.  Averages lie.  They disguise what’s really going on.  What’s more insightful are KPIs viewed through a segmented framework.  Segment your member base on visits in the past 12 months and include KPIs for each segment: number of members in that segment, total visits in that segment, total spending in that segment, visits per member, average check, average spend per member, and the percentage each segment represents of the total in terms of visits and spending.
  5. DON’T presume that members active last year will also be active this year. I’ve seen year to year retention rates vary widely from 30% to 60%.  The rates should be monitored using the same usage-base segments as I’ve described in #4 above, because the higher frequency members will have a higher retention rate.  This should be monitored on a rolling basis so that you don’t need to wait a full year to see if things are getting better or worse.

Loyalty programs need a periodic review and tune-up to ensure that they’re not slipping off course.  It’s a health check that involves analysis of the program and its detailed member, transaction, redemption and campaign data.  The insights will help get your program on course with a roadmap to drive better performance, increased visits and increased sales.

NEED HELP? Contact me.

Loyalty Data Analytics Tutorial – uploading CSV file to MS Access

We’ve been conducting loyalty program analytics for many years and are setting out to make analytics approachable to tech-savvy marketing people using desktop tools.

The examples we will use are based on restaurant loyalty programs, but the principles apply across other industries. It’s all about who, what, when and where. The ‘who’ is the member of the program. The what is actually the ‘how much’ and that is how much the member spent. The ‘when’ is the date and time of the transaction. The where is the location, assuming this is a multi-location type of business. It could apply to restaurants, retailers, hotels, golf course, whatever. You get the idea.

We have worked with about half a dozen different loyalty platforms; the systems that collect and organize the data that supports a loyalty program (who bought, what they earned and what they redeemed). These platforms have some way of exporting transaction data, usually defined as a detailed ‘report’ that can be exported as a CSV (comma separated values) file or an Excel spreadsheet. Keep in mind the Excel option is typically not practical because of its maximum row constraint of one million.

For many medium-sized application we use Microsoft Access to handle up to 10-15 million rows of data, depending upon the size of each row. In this first episode we’ll walk you through loading CSV data in Access and getting your hands dirty.

As always, feel free to reach out to us with questions or if you need help.

Email Dennis at Loyalogy Dot Com.