Great Expectations — Is Your Proximity Marketing Measuring Up?
ShopAdvisor's Jeff Papows explores the best ways to measure the value of proximity marketing efforts.
As we turn the corner for the final sprint to the end of the year and finalize plans for 2017, it’s time to look back on the year and see how we measured up against our goals for 2016. In the future we may look back on 2016 as the year in which mobile proximity marketing really started to go mainstream. Harkening back to Geoffrey Moore’s seminal book on technology adoption you could say we crossed the chasm from early adopters to the early part of the mainstream market.
When I look back at stories about proximity marketing written in 2015, subjects like beacons, geofencing and other location-aware “infrastructure” issues dominated. And though the majority of retailers were and are still rolling out their infrastructures in 2016, stories about platforms, apps, data, analytics and successful implementations began dominating the headlines. Reflecting this trend, market research reports from Proxbook/Uncacast, eMarketer and others paint a picture of a growing market in which results began meeting expectations.
The question now is what is the right way to measure our proximity marketing campaigns? Many are still using traditional techniques including click rates, impressions and foot traffic. While these metrics have merit, they don’t get us to the fundamental measurement metric we want. Is there truly measureable ROI in terms of customers acquired and product sold? Just as mobile proximity marketing is introducing new paradigms for connecting with consumers, we must match that with newer, more concrete ways of measuring the impact of these campaigns. This begins by asking a different set of questions before any campaign is rolled out, including:
- How many times did it convert users into customers?
- Did this marketing campaign increase customer spend over time?
- Are my campaigns turning off user/customers?
- Did this marketing campaign affect long-term engagement?
- How much better off are we now compared to when we started to run these campaigns?
If we apply the KISS principle we can boil measurement down to three fundamental criteria: engagement, revenue and retention. With that in mind, a great technique that will enable marketers to show measureable ROI and make management and the bean counters happy is sales lift analysis, which checks the box for all three criteria. When implementing a proximity marketing campaign with sales lift analysis as a measurement objective there are several elements you should be including:
- Begin by establishing a baseline. Is this a new or existing brand or product? If it’s an existing product, identify a timeframe, geography and stores where it has been available in the past and capture the historical unit and sales revenue data you want to compare against. If it’s a new product you won’t be able to compare against past performance, but you can still compare activity across various regions and store locations.
- Establish your test and control stores. Let’s say you begin with ten stores, all selling the same item. For eight stores you run the mobile proximity campaign but for the other two you do not. It’s also very important to consider other elements when choosing these locations. Buyer behavior can be greatly impacted by other factors including weather, seasonality, population density, demographics and economics. The bottom line is to think critically about all of this to ensure you are making as much of an apples-to-apples comparison as possible.
- Establish your sales lift measurement criteria. The basics include impressions, units sold and revenue generated against which you will measure lift percentage over time. And speaking of timing, the more granular you can be about this the smarter and more effective your campaigns will be. For example, being able to identify the days of the week and times of those days that purchases occur the most enables you to turn the dial on your campaigns up or down to match and further maximize your campaign ROI.
- Don’t forget to take into account the potential halo effect of your campaigns. This can happen in a couple of ways. Let’s say you are promoting a brand with several individual sub-brands and specific UPCs. If you can track down to the UPC level you can see how specific products are popular combinations and/or pull additional product sales along with them. For example, we had the opportunity to work with an Ad-Tech partner on a campaign for a national snack food manufacturer that introduces customer inspired flavors every year which are voted on by public. Through our sales lift analysis we not only determined which of the new flavors were doing better versus the others, but we also found that the overall campaign also lifted the sales for the traditional product family on the whole.
So, you can see that lift analysis can provide the very specific and measureable data to determine the effectiveness of a campaign. But it can be so much more than a forensic, after the fact assessment. It can be used to assess campaigns while they are running so you can make quick, real-time adjustments. Doing so can not only lead to more revenue and greater customer retention but it can also reduce overall costs as you turn campaigns up or down, on or off, thereby gaining greater control over things like ad spend. You can also learn things such as which messages and techniques garner the highest engagement and sales conversion rates with particular customer segments across various regions and time frames. Ultimately, this makes you a smarter marketer and more valuable to your organization.
Every new year gives us the opportunity to start anew. We leave behind the things that didn’t work or dragged us down and focus on the positive and commit ourselves to making improvements. If we apply this thinking to our campaigns and make sales lift analysis a requirement then we might look back on 2017 as the year in which our great expectations for mobile proximity marketing were delivered.
*Jeff Papows is the CEO of proximity marketing platform ShopAdvisor. He has led public offerings, four mergers, and consistently contributed to a high return on invested capital or market capitalization throughout his 30-year career. Jeff is widely credited for growing the industry dominant standard in web-based messaging as CEO of Lotus Corporation, and has also held senior management positions at Cognos, Software International and Cullinet Software.