With shoppers looking for a bargain, many brands will see a significant jump in both new customers and total revenue. These metrics make it appear that these sales are really successful, but how do we know how truly successful these sales really are?
When judging the effectiveness of our sales it is crucial we look at two key measures:
1. Incrementality – how many of the people who bought from our sales would have bought anyway, and at full price?
2. Long-term value – how many of the new customers we acquire during sales do we ever see again?
Luckily for DTC and e-commerce brands, it is possible to get good answers to both of these questions.
Let’s look at incrementality first:
We know that some people who buy from sales were already considering a purchase (had signed up to email lists, followed on socials, engaged with ad content, etc.) but the sale helped nudge them to purchase. But some of these people would have bought anyway at a later date, and potentially at a higher price.
To know how incremental our sales orders are, we need to determine a baseline. The easiest way to do this is to compare our daily sales during the sales period to average daily sales in an earlier comparable time period. That would give us the baseline (see the blue bar in the graph below) to compare again our total daily sales (top of the green bar in the graph below).
This will give a really basic increase in revenue and orders, but we likely increased marketing and email activity to promote our sales so we need to account for that in the baseline.
If you run modelled attribution then you will know the incremental impact of your email and marketing outside of sales activity (I have spoken about how to do this previously here), this gives us the orange and grey bars which allow us to to get a much better understanding of true incremental daily sales.
So for example:
- Step 1 - Before BF we might have averages £26k per day
- Step 2 - We increased paid marketing and email activity around BF so we would have expected an extra £8k sales per day from this activity
- Step 3 - So now we have a weighted baseline of £34k sales per day if we didn’t run any offers
- Step 4 - We actually saw £51k sales per day during BFCM, which gives us incremental daily sales of £17k sales per day. (£119k over a 7 day sale period)
But we also know that sales tend to drop once a sale ends. Many customers who may have bought at a later date push their purchases forwards due to the sale, so our incremental daily sales include some sales “borrowed” from the future.
If we deploy the same projected baselines for the time period after our sales end we can see this drop effect as daily sales plunge below the baseline (see the red box in the graph below) after the sale period has ended.
If we subtract the negative impact from post-sale, from the positive incremental sales during the sale then we get a much clearer understanding of the net incremental impact of our sales activity.
- Step 5 - We can use the same £26k sales per day as our starting point
- Step 6 - We ran less paid marketing and email after BFCM so would expect £4k fewer sales per day
- Step 7 - We have a weighted baseline of £22k sales per day if we hadn’t run offers
- Step 8 - We actually saw £17k sales per day for the 5 days after CM, so we saw a net negative impact of £5k per day.
- Step 9 - We can subtract the £25k of sales pulled forwards (£5k per day for 5 days) from the £119k of incremental sales to get a real incremental value of £94k.
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But getting new customers is only half the battle. We also need to look at the second part of the impact of sales, which is the long-term value of the customers we acquire from BFCM sales activity.
Here we can lean on the customer cohort data that should be at the centre of any digital brand.
We first need to isolate the customers who purchased during the sale (both new and repeat customers) and compare them against a similar cohort of customers who bought outside your BFCM sale.
After this we need to make sure that the comparable cohorts bought similar products, in a similar timeframe and from similar acquisition channels. We then monitor the repeat purchase behaviour of the sales customers against their non-sale lookalikes, if we compare the purchase behaviour of these customers 3 to 6 months after their offer purchase then we can predict their lifetime value (frequent purchase products and subscriptions should get accurate predictions after 2-3 months, while less frequent products may need 6-12 months to see differences in these cohorts.)
That should give you something like this:
So as you can see in this fictional example, our Black Friday sale attracted both new and existing customers successfully, but the new customers were not as valuable as our usual new customers. But the returning customers who purchased in the sale went on to be more valuable than similar repeat purchase customers.
By implementing both of these data measurement techniques you can determine the true value of your newly acquired customers, and how many you would have got anyway. Only by doing this are you then able to understand the real value of your BFCM sales activity. You may find that your impressive sales spikes might be misleading or you may even uncover a new, powerful customer cohort to target.
Let us know if you try any of these measurement techniques for yourself. We’d love to hear what you uncover or have learned!
If you would like to learn more or need guidance on how to accurately measure your BFCM sales activity, drop us a line at firstname.lastname@example.org - we’d love to meet you.