Phy-gital Roundtable: Breakfast Roundup from Germany and Netherlands

02 May '15 | Debjyoti Paul

German Shoppers: Meet Them in the Fast Lane to Phy-gital

15 January '15 | Ralf Reich

Shoppers Will Share Personal Information (But They Don’t Want to be “Friends”)

15 January '15 | Anil Venkat

Modernize or Perish: Property and Casualty Insurers and IT Solutions

14 January '15 | Manesh Rajendran

Benelux Reaches the Phy-gital Tipping Point: Omnichannel Readiness is Crucial

13 January '15 | Anil Gandharve

The New Omnichannel Dynamic: Finding Core Principles Across Industries

13 January '15 | Debjyoti Paul

Technology does not disrupt business – CIO day 2014 Roundup

02 December '14 | Anshuman Singh

Apple Pay – The Best Is Yet To Come

02 December '14 | Indy Sawhney

Digital transformation is a business transformation enabled by technology

01 December '14 | Amit Varma

3 Stages of FATCA Testing and Quality Assurance

06 October '14 | Raman Suprajarama

3 Reasons why Apple Pay could dominate the payments space

18 September '14 | Gaurav Johri

Beacon of Hope: Serving Growth and Customer Satisfaction

05 August '14 | Debjyoti Paul

The Dos and Don’ts of Emerging Technologies Like iBeacon

30 July '14 | Debjyoti Paul

What You Sold Us On – eCommerce Award Finalist Selections

17 July '14 | Anshuman Singh

3 Steps to Getting Started with Microsoft Azure Cloud Services

04 June '14 | Koushik Ramani

8 Steps to Building a Successful Self Service Portal

03 June '14 | Giridhar LV

Innovation outsourced – a myth or a mirage or a truth staring at us?

13 January '14 | Ramesh Hosahalli

What does a mobile user want?

03 January '14 | Gopikrishna Aravindan

Trade Promotion Analytics – Part 1

Posted on: 11 February '11

Trade promotion is a very important marketing vehicle used by Consumer Product Group companies to stimulate demand for their categories across their channels. In many cases, trade promotion is the second largest expense after the cost of raw material and finished goods itself. By many estimates, CPG companies spend anywhere between 8-14 % of their turnover and up to 60% of their marketing budgets on stimulating demand on channels. Typical examples of trade promotion investments include:

  • Buying strategic shelf spaces which have high visibility in stores such as Walmart
  • Price cuts for 7-11 stores to increase share of shelf for high margin products
  • Investments in in-store signage assets like danglers
  • Gifts bundled with the products at duty-free stores for perfume/liquor and cigarette packs, etc.
  • In-store sampling at select stores for the new ice cream flavor

Advanced analytical constructs like optimization, regression, “what if” analysis, geo-spatial modeling and text mining can provide significant visibility into the effectiveness of these trade promotions spends. The information attained can provide insights in terms of sales uplift contributions, and can help in optimizing the same in the face of many real world constraints during the fund allocation process.

Before examining how these constructs can be applied, however, it’s important to understand the various business scenarios and unanswered questions that channel managers, brand managers, category managers and financial analysts (CFO office) need to address while evaluating promotions on which they have spent millions of dollars. Most of the questions, as seen in the examples below, probe the impact of the promotion on the overall category/product uplift and the resulting RoI achieved in addition to its effect on other products and categories.

  • Based on historical promotion data, which are the top 3 promotion vehicles that have maximized uplift and helped launch/re-launch a product?
  • If I want to increase my sales uptake in the West Coast market for my organic products, do I purchase more strategic shelf-space at Walmart or do I give a 5 % price cut at Shoprite?
  • What’s the percentage uplift in sales during a seasonal period compared to a non-seasonal period?
  • Can we quantify the rate of cannibalization of private label brands on our premium flagship products as consumers become more price sensitive during tough economic conditions?
  • Did promoting a sachet package of a shampoo cannibalize a strategic SKU that is a “Cash Cow” for us?

In answering the question above, CPG organizations may find the advanced analytical constructs of optimization, regression, text mining, geo-spatial and “what if” very useful. Each offers its own strength in helping evaluate trade promotions as outlined below.

1. Optimization construct
If a channel manager wants to allocate his trade promotion funds between shelf-space procurement and price cuts, he needs to determine the appropriate ratio to maximize category uplift. Factors that need to be taken into account include the seasons when a promotion can be run, fund constraints for each particular promotion, channel constraints, etc. An optimization tool can be configured to discover the best possible breakdown of funds given these factors.

2. Regression construct
A regression construct can help pinpoint both positive and negative drivers of category uplift. A sudden spurt in private label sales or a competitive product could be a negative contributor to a strategic SKU that had been the leader in its category. For example, let’s say a category manager has a hunch that the newly launched pink colored cold cream, which is heavily promoted on select channels using the shelf-space vehicle, is beginning to cannibalize the traditional cash cow, which is a white colored cold cream. A regression construct can be used to model the cannibalization and statistically confirm the presence of the same. It can also ascertain the impact of individual trade promotions on the sales uplift and rank the highest contributors to volume uplift.

3. Text mining construct
Imagine a scenario where a crucial trade promotion investment decision needs to be made to promote the launch of new shampoos for Latino markets. The product/category manager wants to dive deep into the key learning themes of past promotions of similar nature by examining comments that were entered into a digital promotion post-harvest application. An unstructured text mining process can be run on those comments to synthesize the top 5 themes and distill the essence of the learning’s by providing a theme map, such as the one pictured below.

4. Geospatial construct
If one has outlet-level granularity for promotion and store data, then the information regarding store promotion metrics can be rendered on a geo-spatial map. This helps the channel and category manager ascertain if location and store catchment information can explain behavior regarding promotion effectiveness.

5. “What if” construct
Let’s say a perfume manufacturer wants to heavily promote a particular perfume in all airport duty-free stores using varying price cuts. The effects of these price cuts can be effectively modeled using the “what if” construct, which can provide estimated incremental volume uplift to be expected from promotions/pricing scenarios planned in the upcoming season. A pricing “what if” scenario can provide clear visibility into unwarranted price concessions for selected channels/categories.

With the help of these five constructs, managers of Consumer Product Group companies can leverage their custom requirements and accordingly channelise their energies on optimising their trade promotion activities. This will result in enjoying a much more focused trade promotion practices, keeping in mind the needs of all those involved in the trade promotion cycle.

In my next blog, I will be presenting some view points on how to implement best practices at the organizational level. These practices will help a CPG company to wisely allocate expensive trade promotion funds.

The author would like to thank R. Geetha, Anil Kumar, Reshma Dash, Neha, Geetha and Pratibha for their valuable inputs.