As most business people will/ should know; the most important “post implementation” phase of any given marketing activity is to monitor and measure the response from the market. However, what many business people do is only focus on measuring market responses in relation to the specific products that featured in the given marketing campaign.
Here’s a bricks and mortar sales conversion and sales performance monitoring/ measurement approach that is used by some of the best in business – in fact these indices (KPI’s) are considered by these businesses to be their most important operational level KPI’s to track. They consider:
a) The relationship of product units sold to number of visitors. i.e. sales conversion.
b) The contribution of a particular promotion/ campaign to overall sales for the given measurement time frame.
- SALES CONVERSION –
Here we’re interested in the extent to which visitors to a physical store ultimately end-up buying a product. This KPI measures the sales conversion effectiveness of sales people. It is the rate at which visitors are converted into buyers.
Monitoring of visitor statistics is made easy in this day and age by the use of electronic foot traffic counters. I used to source such counters from a Taranaki based supplier. These counters feature an infrared beam, which when intersected/ broken by a person walking through it as they enter/ exit through a doorway cause a count figure to register. i.e. 5 x incidences of a broken beam would register 5 x counts, 140 x incidences of a broken beam would register 140 x counts, and so on.
I implemented foot traffic counters in a national franchise during the 1990’s; but despite this technology being around since then I observe that it is still the minority of businesses that use them.
Here’s how to calculate the sales conversion rate using these electronic counter…
If our counter registers say 2300 for a month, this figure represents our “raw data” foot traffic count figure. We need to adjust this raw data figure to reflect the fact that during the course of each day:
a) A visitor both entered and exited the store.
b) Some visitors entered and exited the store with an accompanying person who was not a prospective buyer.
c) Some visitors entered and exited the store with a pram and/ or child – again, who were not prospective buyers.
I used to guide store owners that a divisor of 2.5 is a realistic statistical adjustment to make to a raw data count figure, to then derive the total number of visitors who entered the store given the measurement time frame under review. So using the above example, this would mean that the Total Number of Visitors for this month would be 2300/ 2.5 = 920 visitors who had the potential to be a buyer.
For the particular industry that I was involved with at the time, historical data confirmed that on average every time a customer would commit to buying a product, they purchased 1 (one) product per purchase occasion. Knowing this second piece of information allows us to proceed to perform the sales conversion calculation…
Sales Conversion Rate = Total Unit Sales divided by Total Number of Visitors
So using the above example and a Total Unit Sales figure of 250 units for the month in question…
Sales Conversion Rate = 250 units/ 920 visitors = 27 %
This means that for every 100 x legitimate (potential) customers who entered this store, 27 customers ended-up committing to purchase at least one product per purchase occasion.
Once we know what our Sales Conversion Rate is, it then becomes possible to make future predictions around sales results…and use these sales estimations in our budget setting process.
For example, based on a Sales Conversion Rate of 27 %, if the Number of Visitors we were to attract into store was to increase by 10 % in response to next month’s promotion, Total Unit Sales was expected to remain at 1 x product per purchase transaction and we will be targeting an average sale value of $30, what could we expect the resulting sales revenue to be ?
Answer:
Expected Total Number of Visitors = 920 x 10 % = 1012.
Expected Total Unit Sales per customer = 1
Average Sale = $30
Estimated Sales Revenue calculations…
a) 1012 x 27 % = 273 x visitors who are predicted to end-up committing to purchasing at least one product each
b) 273 buying customers x 1 product sold per transaction x $30 average sale = Total Predicted Sales of $8,190 for next month
2. CONTRIBUTION OF PROMOTED PRODUCTS TO OVERALL SALES –
This is the relationship that many business people overlook, and instead only concentrate on analysing the performance of promoted products in isolation of overall sales.
Business owners should be mostly concerned with understanding how a particular promotion contributed to the overall sales of their business. Why ? Because often, despite marketing collateral (e.g. catalogue) invoking a particular purchase need/ desire in the mind of its recipient, once in a store environment they can be influenced by either their own change in preference and/ or the persuasion of in-store sales people to end-up purchasing a product that is different to that which initially caught their attention and caused them to connect with the business.
If a particular promotion resulted in sales relating specifically to promoted products of say $30,000 and the overall sales achievement for this business was $350,000 for the measurement period, this would mean that promotional sales contributed 8.6 % of overall sales. Some might deem this to be a low contribution relative to a level of marketing spend of say $70,000 that it took to generate this level of promotional sales.
However, if we then consider that overall sales for the same measurement period LAST Financial Year were $310,000 we quickly understand that overall sales grew by $40,000 for the same period this year. An increase of 12.9 % ! An increase that was arguably helped along somewhat by this year’s marketing promotion.
Rule of Thumb: Be more concerned with looking at “overall” financial outcomes in relation to products that feature in a promotion – even those which relate to “product categories” as a whole – rather than statistics that profile individual product performance.