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How to Implement Analytics-Driven Pricing Strategies for B2C Firms

Mar 4, 2018

Implementing Analytics-driven Pricing Strategies

The pricing strategies adopted by companies across sectors depend significantly on their business goals and the competitive landscape. Companies ideally seek to maximize value and market share by selling the most profitable products through the most efficient channels. Pricing strategies critical determinants of whether the business succeeds or fails, which is why it is a strategic priority for every organization. Furthermore, in a B2C scenario where consumers have ample substitutes for a category of products, price becomes an essential parameter for comparison and arriving at the final purchasing decision for a consumer.

Here is a step-by-step description of analytics-driven pricing strategies for consumer packaged goods in the B2C market :

1:  Establish Price Tiers

A product category can be broken down into three tiers according to consumer purchase patterns, which include value, mainstream, and premium. Companies must have a unique offering for each tier to compete at each level actively. Any product offering with a price that takes it across tier boundaries will just create confusion among consumers and harm value creation.


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2: Price Modeling

Once the price tiers are established, the next step in formulating pricing strategies is to undertake price modeling. Since modeling individual products could prove to be a complicated process, a more efficient approach is to select the power product. Those that generate maximum sales (volume) or market share per unit of distribution cost is referred to as a power product.

3: Price Sensitivity Analysis

Price sensitivity analysis helps to understand the impact of different pricing strategies based on various factors apart from competitor offerings. B2C companies need the right analytics in place regarding how the consumers are likely to react towards different price points. This can be achieved by developing regression-based log-log models for product elasticity, cross elasticity, and cannibalized product offerings.

4: Analyze Channel Efficiency

Retailers often resort to pricing strategies such as deep-discounts to stimulate demand. Advanced analytics techniques like machine learning can help firms customize the discounts they offer based on product preferences and regions. Securing shelf space with channels/retailers is another aspect of channel efficiency. Retailers must be aware of which retailers/channels generate the most revenue per unit cost (the retailer margin).

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5: Pricing Per Pack

Most of the consumer products are sold in a range of pack sizes, with more significant discounts for bigger packs. Hence, companies need to evaluate relative pricing strategies across various packs to ensure that they don’t go overboard in offering discount while maintaining an efficient positioning against competitors in every price tier.

6: Evaluating Promotional Activities

From building long-term brand equity to offloading end-of-life products and defending market share against competitors, there are numerous reasons for a brand to run promotional activities. Each promotional activity undertaken goes through several levels of the decision-making process regarding the type and the depth of the promotion such as bonus pack, multi-pack, and buy one get one free, etc. To make all these decisions optimally, businesses need to evaluate the short- and long-term sales uplift they get from promo campaigns. This can be done through pre-, post-based test control full-factor design experiments.

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7: Leveraging Analytics

Firms mainly want to use pricing strategies to maximize their profits or outlet sales at the brand or segment level to constrain market share. Companies can use statistical optimization and analytics techniques to keep a check on the effectiveness of their pricing strategies. This will also help them to continue the successful pricing strategies and take down the unsuccessful ones.

 

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