Contextual AI Asset Price Optimization
A leading brewery wanted to understand the impact of price change on product sales to develop a pricing strategy across SKUs and maximize the top line. The pricing team faced the uphill task of designing a unified pricing strategy across 200+ SKUs manufactured worldwide, without causing a negative impact on sales.
To understand how this problem statement was solved, we’ll first take a look at how beverages manufacturers would approach a price optimization problem, and the various market factors that collectively impact prices.
Price sensitivity is of keen interest to sales managers in the beverages market, given how the smallest price variations can change consumer perception from ‘a real bargain’ to ‘too expensive’, upending sales and market shares alike. Price optimization exercises may become essential while extending existing market share, defending from new product entries, or breaking into a new market altogether. Price modelling is of essence to product managers as well, as it can guide new product development roadmaps, go-to market strategies, assessing CLTV, ROI, market share, among others.
Typically, prices are determined factoring in multiple aspects, so it hits the sweet spot where neither sales nor revenue figures are sabotaged; it takes an understanding of what would appeal to customers as the ideal price (presuming most customers have an elementary understanding of a product’s worth), while tuning into optimal quality, quantity, features, competitor pricing models etc., at said price point, to nudge purchase action, without compromising on business costs. Then again, pricing cannot focus on business costs alone, but also tune into capitalize on market movements, product demand, among other factors. It isn’t uncommon for businesses in the beverages industry, more so new entrants in saturated marketplaces, to align prices within the range of competitor prices, with minor price discrepancies. Of course, there are other factors like product affinity, availability of substitutes, purchase frequency etc., that govern price elasticity and in turn buyer decisions.
The beverages industry particularly notices alcoholic drinks in high-price categories, to be inelastic for the most part - underpinned by loyal patrons and understandably low purchase volumes given the hefty price tag. However, a dynamic pricing strategy that accounts for varying market factors usually comes into play with relatively inexpensive beverages that are prone to a higher price elasticity, as in the case of energy drinks, fruit juice etc. A price-conscious customer, who would predominantly lean towards inexpensive beverages, assuming he/she already has an affinity towards the product, would also find it easier to switch brands in the event the price of the desired product increases. The price elasticity of beverages even varies significantly across markets, emerging economies and developed countries; for instance it’s seen that alcoholic drinks are particularly prone to greater swings in consumption volume owing to price elasticity in developed countries when compared to developing countries. It's evident that multiple factors are at work in governing how pricing affects purchase decisions. Let’s delve into what price optimization entails in the beverages industry.
The saturated disposition of the beverages market renders traditional pricing and revenue management strategies unproductive. Without data, it is difficult to gauge purchase drivers – whether it’s a shift in consumer purchase pattern or promotional offers, price changes in competitor products, or more. With multiple market factors at play and strong competition at its heels, beverage manufacturers are leaning on advanced analytics to strategize pricing and in turn manage revenues. Price Optimization AI tools help in growing short and long-term sales prospects with insights on the most optimal pricing, thereby helping beverage businesses to boost revenue and manage sales more effectively, in an otherwise highly competitive market.
We look at data-driven Price Optimization tool essentially as a contextual AI asset, where it is contextualized to the specifics of the beverage manufacturer, such as data, processes, etc., and accompanying market dynamics. Our proprietary platform Co.dx, enables our beverage price analytics experts to coodify algorithms, by leveraging past learnings in the F&B market, and deliver a contextual AI asset that offers a well-rounded view of price movements and pricing recommendations to maximize margins.Let’s take a look at some essential capabilities of Price Optimization tools
A smart price optimization tool offers insights to optimize prices across consumer personas and markets by predicting actual incidence rates for a given price point. Design thinking frameworks further help in making a beeline from customer personas to packaging preferences, price sensitivity and more, to devise a holistic pricing strategy.
Given how product categories are susceptible to multiple market factors that cause prices to oscillate, it’s essential to assess impact from price changes in SKU volumes, volume shift to other SKUs, launching new SKUs in the market, and so on. A smart view of SKU dynamics allows sales teams to maximize volume/ marginal contribution.
With multiple product categories speckled across geographies, it becomes essential for sales managers to understand volume outlook based on different product-location combinations. The tool will have to factor in hyper-local market variables to get a comprehensive and more importantly realistic views of buyer persona and behavior.
A buyer behaviour simulation tool helps decision-makers to visualize impact and answer various questions associated with pricing. A smart pricing model also unearths insights on opportunity cases to boost revenue in existing product categories, responding to changes in competitor pricing, and other market movements.
Consumer price sensitivity is measured across categories and geographies using conjoint analysis. The indirect pricing technique helps in understanding how consumers perceive or value different facets/attributes of a product, which in turn influences their purchase decision. The proceeding results can then be used to chart out pricing models for individual markets.
Conjoint analysis, a market research technqiue, is helpful in discerning consumer decision, by weighing in both statistical and real-life aspects. Typical survey methods are used for conjoint analysis, presenting different products from owned and competitor businesses across price points to sample shoppers, to guage purchase affinity in each category. Sometimes, integrated VR platforms may be used as an option to conduct surveys for conjoint analysis, where simulated shopping setups have consumers choosing products on different screens.
It’s equally important to tune into market dynamics in different geographies. Conjoint surveys can be extended to sample customers in various geographies to understand price-location combinations conducive for sales. Here again, mining data on segmentation variables that are telling of customer persona such as economic indices, demographic factors, behavioral economics etc., across market variables like product features, packaging, advertising etc., helps in making realistic projection of sales volumes in different markets.
The survey results are modelled to gain a keen view of consumer preferences and price sensitivity, and accordingly optimize prices & distribution to maximize volume and marginal contributions. Purchase decision hierarchy model is developed using focus group data, by quantifying the utility of product attributes through multinomial discrete choice models.
The pricing model is developed for different product-location combinations, visualized in an easy-to-use dashboard tool. Socialization of results, duplicating pricing models, etc., can help in scaling tool adoption across geographies at speed.
Now that we have a grasp of what goes into the workings of a price optimization tool, let’s now take a look at how the aforementioned brewing giant utilized a Price Optimization Contextual AI tool across geographies to discover revenue opportunities worth hundreds of millions.
A leading brewery wanted to develop a unified pricing strategy across 200+ SKUs manufactured worldwide, without causing a negative impact on product sales
TheMathCompany utilized conjoint analysis survey and discrete choice modeling techniques to determine optimal pricing and distribution of SKUs across geographies for the beverages and brewing manufacturer.