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Promotional Pricing
Improving the ROI for an electronics retailer
Promotional pricing is an effective tool that businesses (both B2C and B2B) can implement to attract customers with special offers on their products or services. Typical forms of promotional pricing include:
- Discounts - reducing the price of a product or service for a limited time period
- Bundling - offering multiple products or services together at a lower total price compared to purchasing them individually
- Coupon codes or loyalty programmes - offering discounts on future purchases for returning customers
- Rebates - providing customers with the opportunity to receive a portion of the purchase price back after they make a purchase
When implemented successfully, promotional pricing can help a business to launch a new product, capitalise on seasonal trends, acquire new customers, position themselves ahead of competitors, etc. However, there are many challenges to doing it right, for example:
- Offering products and services at a discounted rate can lead to lower profit margins
- Frequent use of promotional pricing may lead customers to perceive your products as lower in value or quality
- Businesses that rely heavily on promotional pricing might be seen as a discount brand rather than premium
- Regular promotions may lead customers to 'predict' when promotions are happening and reduce purchasing during non-promotional periods in order to game their purchasing
The key takeaway is that promotional pricing is an effective tool that can hugely propel a business forward when done correctly, however there are many factors to consider. This is where data & AI becomes useful.
We present a model for determining the revenue & profit impact of a promotion below
The model above takes the context of a classic promotional offering. In this situation there are a few factors that are baked into the model
- Promotion type - is this a typical promotion discount offering? are we bundling different products or services? is this a loyalty programme?
- Level of discount - how much are we discounting the product or service by?
- Length of discount period - for how long is the promotion in place?
- Baseline sales - this is the most important driver of the model, here the algorithm predicts what would be the baseline sales should the promotion not have been implemented
The accumulation of all these factors leads to isolating the net effect of promotions on both revenue and profit
We can break down the impact on baseline sales into more detail
The figure above shows the impact of promotional pricing in the context of a business selling a product. In this situation there are four effects to consider
- Promo sales uplift - the additional revenue generated from offering a product at a discounted rate
- Anticipation effect - customers may be able to anticipate when a product is about to be promoted and reduce their consumption in the build up. This generally happens when promotions coincide with popular holidays / seasons e.g. Black Friday
- Accumulation effect - customers may have wanted to make the most of reduced prices during the promotion period and purchased a higher quantity of the product during the promotion. As a result, they reduce consumption immediately afterwards
- Cannibalisation effect - this is a loss of sales of other similar products as a result of a company introducing a promotion. This generally occurs when there is some substitutability between the product on promotion and the other SKUs
The net impact of promotions is then calculated by the model, as shown on the righthand side
The above illustration shows how this can be displayed in a decision cockpit. In this situation our algorithm calculated a return of incremental profit per $ invested of promotion budget of 87%. This was calculated by taking the net promo effect, considering the four factors explored above and comparing it to the cost of implementing the promotion.
The model also has a predictive element whereby it identifies possible scenarios to improve ROI - in this case a reduced promotion level of 8% could have been substantially more profitable.
