The big question a retailer should be asking at the end of every promotion is ‘which levers did this promotion pull and what can we learn which informs our next promotion?’.
The question is seldom asked, with most retailers feeling pretty happy if they can get by knowing that sales were up or down. However for all the money, logistics and heartache that goes into promotions we owe ourselves to be asking – and answering – more than this.
The common theme from medium size retailers to very large ones is how little category managers, marketing and other executives truly know about the effect of these promotions which they mechanically work through month in month out. There would also be quite a few organizations where there is a disconnect between what the people at the highest level of the organization believe is happening, and what is actually going on. If you are a high level retail exec reading this, and you believe that your organization is better than this, I’d challenge you to spend some time and test whether your assumption is in line with the reality on the front line. For those few that do it, the next rude awakening is how much time it consumes to pull something like this together, meaning that it is done rather selectively.
It generally doesn’t happen for a number of reasons, but the overarching theme could probably be described as this: While the data is generally there to be able to analyze the promotion at quite a granular level, the problem is that the pain involved in doing this is more than the category manager and other ostensibly interested stakeholders are willing to bear – and rightly so in most cases.
TOWARDS A SOLUTION
Below we show the sort of analysis that can be applied to a promotion which certainly helps to get a better understanding of the investment. We will create a decomposition of the promotion to give a true sense of what was responsible for the lift in promotion and what wasn’t. All the data to do this is owned by any retailer who operates a loyalty program.
The example is from a module in 11Ants RIC (Retail Insights Cloud) which allows complex analytics to be applied to raw data, with an entire analysis being performed in less than a minute, and which can be run by a person in the business with no technical expertise. As an introduction, we are interested in understanding the impact and true contributors to sales growth that were generated by a hypothetical Coca-Cola 2L promotion held on a certain date.
Getting started – entering the inputs into the Performance Module:
This part will only take ten seconds to read, but is important as context to the more interesting part which is the output immediately below that:
1) The starting point is to select the date range that the promotion was run on, and then to select a like for like comparison period (e.g. same week one year ago).
2) The second step is to nominate the focus product, category or brand. In this case we will be quite specific, we are looking at the effect(s) of a promotion on 2L Coca-Cola.
3) The hard work is now done, the final step is to hit the button ‘Analyze Promotion’ and wait approximately 60 seconds while RIC constructs a powerful model of the promotion.
Resulting in the following model outputs:
To walk through the elements of the above:
REVENUES
We can see that revenues were up by 76.11%. Which is fantastic, but it begs the question – why? Which is exactly what we will be able to answer by working our way down various branches of the tree. Keep in mind that to perform this level of analysis requires that a retailer has the ability to link a unique customers purchases over time. This is normally achieved through the use of a loyalty program; however even for retailers with loyalty programs of course not every transaction is made by loyalty card member, so these transactions are excluded from the analysis. The information about these transactions is in grey below where it says ‘Excludes Untracked Revenue’: $1.69k and describes Percentage this Represents: 13.92%. This information is just there to balance out the numbers, the sum of the two amounts will be your overall revenue for the promotion period.
Firstly we will focus above the dotted green line – which focuses on the question ‘How many customers did the promotion reach?’
# OF CUSTOMERS WHO PURCHASED FOCUS PRODUCT
If we follow the upper path of the tree, the next node is the number of customers who purchased the focus product, which is up by 29.91%. Again begging the question – why? Is it new customers, the same customers, is it increased traffic in the store, or increased penetration of traffic in the store? One of the things we can see in the grey text below is that we gained 467 new customers, retained 89 customers and lost 339 customers.
# OF CUSTOMERS IN STORE
It turns out it is certainly not majorly due to additional customers in the store as this number is only up by 3.99%.
CUSTOMER PENETRATION
This has increased by 24.92%. So yes, a big driver of revenue growth was increased penetration of customers.
Now we will go below the dotted line, where we will drill into the behaviour of customers who engage with the promoted product – answering the question ‘How did the buying customers behave?’
SPEND PER CUSTOMER
Spend per customer was up by 35.56% – which is good, but again – very interesting to understand what drove that.
VISITS PER CUSTOMER
Visits per customer were up by 0.6% (i.e. purchase frequency was up by this amount).
SPEND PER VISIT
The amount of money spent per purchase occasion on the focus product was up by 34.75% – which is pretty significant, and we will naturally be interested to drill down a layer further and understand why this was, which we can:
UNITS PER BASKET
This was up by 102% in the promo period with shoppers taking an average of 3.42 units in that period.
PRICE PER UNIT
This was down by 33.22% in the promotion period. This probably the one fact that we did know about our promotion, but it certainly feels more informative when looked at in the context of all the other moving parts of the promotion.
The impact bar at the bottom is a nice way of graphically visualizing the impact of the various components of the promotion. Everything to the write of the zero line was of positive impact/contribution, while everything on the left was negative. The colour of the segments of the bar are tied back to the titles above various components of the tree (e.g. Visits per Customer is written in yellow and is represented in the yellow segment of the bar.
CONCLUSION
The point of this blog post was to show some of the rich and actionable information which is available simply by linking transactions of customers over time and then applying meaningful analysis to it. This data is theoretically available to any retailer with a loyalty program, and is an example of what we mean when we say that you only start to truly extract value out of your loyalty program when you can use it to understand your customer and their behaviours better. This is an example of how you use your loyalty program data to better inform the significant resource – both time and monetary – which your organization puts into promotions every year.
With the 11Ants Retail Insights Cloud anyone in your organisation can have access to these insights in an easy to run, fast platform. No coding or analytics experience is needed. All your promotion results can be tracked and analysed within minutes.