Four Ways to Improve Promotional Analysis

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Retailers and consumer brands deploy promotions every day in the form of circulars, coupons, FSI’s, direct mailers, digital offers and more. While promotions are becoming better personalized and more multi-touch, the ability to measure the effectiveness of them remains weak.

The main reason promotional analysis is limited is due to the amount of data that has to be integrated to get the total picture view, with this data residing in a myriad of silos. For example, sales data is often captured by POS systems, coupon redemption is often handled by an outside clearinghouse or agency, promotional calendars are often tracked on spreadsheets, and store participation data might live in a separate system that may or may not include all the divisions in a standardized view.

If retailers and CPGs are to know what promotions are truly delivering value to their brand and business, they need an automated process that can bring all of these data sources together into a centralized system that actionable insights can be gained from. Category managers and shopper marketers cannot continue to attempt to manually consolidate and parse all of the attributional data required for promotional analysis. In a report titled “Promotional Analytics in Today’s Retail World,” 1010data lays out key areas retailers and manufacturers should focus on to improve their promotional analysis. Below is their guidance, combined with our own insights from building program management solutions for some of the world’s largest CPGs.

1. Use High Relevancy Data
In order to truly assess the effectiveness of a program, contextual data, like what section of a circular an ad was located in, is necessary. Segmentation data also has to be traced, such as how many versions of the promotions went out to what customer segments. Last is retail execution data, such as whether the POS was featured with other complementary items as a shopper marketing solution, and the POS’s proximity to competitive category promotions.

1010data defines the highest relevancy promotion data as the following:

• Promotion run time
• Amount of product shipped to support the promotion
• How many and which stores participated in the promotion
• Which regions participated
• Past promotions executed at the brand and store level
• Location of item in a circular or mailer
• Size of an FSI
• Size and placement of a digital coupon
• Adjacencies – other items or categories featured with the primary item or
benefiting from the primary item’s promotion

2. Build A Promotional Activity Archive
As a category manager, you should have access to technology that in minutes allows you to know the status of your promotional programs across retailers and the history of program activity at the brand and retail customer level. This program history archive is needed in order to support continuous program enhancement and pinpoint what tactics work best where, and at what times. The archive should not only tell you what programs were executed where and when, but also whether it was a solo or multi-brand program, how many versions were deployed of what tactics, and what the total sales profit margin was with the cost of overhead and redemption subtracted.

3. Increase the Granularity of the Data
Promotional performance data is often available only in summarized form. The data typically does not detail what exact tactics were involved in the program or lumps them all together into a generic category, such as digital, whether than specifying whether it was a digital banner, social post, email blast etc. Program tactics need to be broken out by pre-shop, in-store and post-shop to understand how they effect the shopper journey and to determine the highest value tactic mix. It is this segregation of program data that is needed to reach the right customers with the right offers via the right media. Another way in which retailers need to increase their data granularity is by including the halo effect of a promotion, which is the impact that one item has on the sale of items around or associated with it. This metric is critical to retailers in determining the true value of a program.

4. Measure the Impact on the Total Basket Value
Retailers and shopper marketers cannot measure the performance of a program by incremental sales lift alone, as these figures can be misleading. Instead, they must align the location/placement of the promotional ad (contextual data), with the incremental sales lift, brands included in the program, and the total dollar value of the resulting baskets. It is critical to measure the basket value because even if a promotion is delivering strong incremental lift, if it is not increasing basket size and the discount is substantial, minimal profit is delivered to the retailer and manufacturer. Alternately, a promotion with a minimal discount may have a low incremental lift but a great impact on basket size, making it’s true ROI value much higher. Retailers make the greatest profit with programs that drive the purchase of other items, and you cannot know whether a program is achieving this without basket level detail.

Promotions continue to be a powerful vehicle in today’s retail world. The most analytically advanced retailers and manufacturers are using analytics to understand every aspect of their business, and are now putting the focus on promotions. The winners will be the ones that use data to understand how their promotions impact trips, basket size and sales of adjacent categories.

To learn how Cierant’s shopper marketing program management technology can give you the promotional analytics you need to optimize performance, please call 203-731-3555 or email inquiries@cierant.com. Our shopper marketing solutions eliminate offline, spreadsheet tracking and allow shopper marketers to real-time track and evaluate their promotional activity for ultimate performance clarity and control.

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