How To Build A High-Yield Retail Promotion MIX With Advanced Analytics Hemanth Kumar June 25, 2018

How To Build A High-Yield Retail Promotion MIX With Advanced Analytics

How To Build A High-Yield Retail Promotion MIX With Advanced Analytics

Retailers always sit on a humongous heap of data. The problem here is not about quantity, it is about the quality of data and making data work to help them make quick decisions.

Retailers and CPG manufacturers live in highly fragmented markets with thin margins, low average prices, and highly vulnerable brand loyalty.

All these need to be addressed by a sound promotion strategy. However, if the decision-maker is not equipped with quality data and advanced analytics software, a high yielding promotion mix strategy, in theory, would fail in practice.

So, how can Big Data and Advanced Analytics help in determining the perfect promotion mix?

What Is the Best Retail Promotion MIX?

Promotion mix for a retailer constitutes of publicity, sales promotion, advertising and personal selling. However, what quantity of each of these elements should be maintained to attain the desired bottom line is a fact unique to each retailer. This has made retailers to launch more and more promotions in desperation to increase the top line sales. However, the key is to reduce the number of promotions and increase the percentage of value-adding promotions to the mix. The best promotion mix gives the maximum bang for the buck and maximizes every dollar spent.

Big Data and Advanced Analytics are discovering new ways to configure the four elements of a promotion mix for each product that can change in response to the consumer and competitive dynamism. With the aid of data and analytics, retailers can create more successful promotional strategies that are lean, use fact-based processes, and equip with enough organizational capabilities while identifying overlooked opportunities.

Retail Promotion MIX – Promotion Analytics Techniques

Interestingly, more money is spent by a retailer on trade promotions than on advertising. The large spend often reaps low-to-negative returns for the marketer. This is either due to a wrong choice of channel or set of audience. According to Nielsen, more than two-thirds of trade promotions that happen in the US each year don’t break even. However, most of the top retailers who achieve a break-even on their trade promotions, rely heavily on optimizing pricing and promotional strategies using analytics.

Some models that help promotional analytics are discussed below. They also help retailers with better control on inventory levels that improve customer satisfaction while ensuring that the right products are in stock during promotions.

Measure & Optimize Your Trade Promotions Effectively

with Compass: An AI-Powered Trade Promotions Optimization Solution

Trade promotion optimization models

trade promotion optimization model creates an optimal corporate and customer promotional calendar that can generate the desired sales volume and profit without overspending the trade budget. It utilizes highly configurable constraints that focus on timing, frequency, product dependencies, forward buy impact, seasonality, pricing, pre-post promotion timing gaps, etc.

Read More: Trade Promotion Optimization: Challenges and How AI Can Help

Promotional Lift Models

This model utilizes historical weekly data to identify and quantify the impact of discounted retail prices, different forms of merchandising, and seasonality on weekly sales of the end-consumer. To complement optimization of price dependant promotions analytical tools also use price optimization, price elasticity, threshold and gap models.

POS Analytics

To leverage the power of POS data and make most out of Temporary Price Reduction (TPR) campaigns, analytical techniques dedicate specific rules and tools for exclusive POS analytics. This warns a marketer from announcing unrealistic promotion prices that may hurt the bottom line.

Read More: POS Data Analysis: How Can Retailers Make the Most of it

Retail Promotion MIX Strategy – How Can Big Data and Advanced Analytics Help?

Using a advanced analytics, a US specialty retailer has increased sales by 3% despite a nationwide slump in the corresponding category. Similarly, another retailer using analytics recaptured its market share while increasing its sales by 4%. Advanced Analytics can provide actionable recommendations based on past promotions, competitors, primary and secondary sales, syndicated data etc. to provide the right promotion mix.

Retailers can leverage promotional data analytics in the following ways to optimize promotions.

Measure Promotion Effectiveness

Retailers have always struggled with determining the effectiveness and ROI of a trade promotion. Advanced Analytics software not only helps you measure promotion effectiveness with the right and customizable metrics but also tell you why a promotion is effective and if it can be run for a different product or category or location etc.

What-if scenario building

With Advanced analytics software, you can build virtual promotion scenarios and learn the estimated ROI for each scenario. This enables you to customize different promotional and pricing parameters and try different variations of each promotion. Thereby, you can only run promotions which have potential to deliver high ROI.

Drive Category-Level Decisions

For some categories, a retailer may choose to reduce the shelf price permanently while at other times, may reduce the price temporarily with a discount or increase the price with a hidden math. Such decisions are facilitated largely with the help of promotional analytics. This requires the retailer to use big data analytics to create a category-level strategy by identifying promotions that increase sales and margins, that dilute margin but increase sales, that dilute sales only, and with extremely low-impact.

Data-Based Localization Strategy

With offline retail stores, big data analytics can help a marketer to strategize for a better account layout, configuration of shelves, article stocking, managing seasonal items, new brands, SKUs and other elements.

Digital Vs Offline

In an age when digital versions of retail stores become their own rivals by challenging the ease of searchability and user experience for offline customers, deeper data analytics can help in improving store design, floor plans, merchandise mix, and other localization elements.

COMPASS as a single-suite analytics tool leverages Big Data, Advanced Analytics, and AI chatbots technologies allow retailers to eliminate guesswork and recommend the most optimal promotion mix. It supports across multiples teams that handle sales, marketing, and revenue management. Data derived from multiple sources can create complexity in identifying the most performing campaigns.

The software tells which promotions work well, how much should be invested in each promotion, and so on. Advanced Analytics powered in COMPASS harnesses data to support a retailer with recommendations on the best promotion mix strategy by choosing the most appropriate promotions.

If you’d like to learn more about this topic, please feel free to get in touch with one of our experts for a personalized consultation.