The food sector has given us many challenges we have been able to solve
challenge
1

Consumer goods: Baseline annual evolution

A key element of a good strategy for marketing a product is being able to forecast as accurately as possible the demand for that product.

In order to improve demand forecasts, our client needed to know the annual evolution of the baseline of one of the flagship products of the company.

Solution:

Once we have quantified the impact of the drivers that affect the sales of our product, in order to build the baseline we consider the contribution of those levers that were present in the long term, and which could depend on the brand itself or on exogenous factors.

The Baseline would be the estimated sales in the absence of promotions and advertising.

Actions carried out:

According to our analysis, an increasing trend was observed in the weekly average of the Baseline, mainly caused by a reduction in the price differential of the analyzed brand with respect to the brand of the distribution, which reduced the negative impact of the crisis on sales.

Results:

The demand planning department of our client, improved the demand estimation of one of the main products of the company. It also strengthened pricing strategies since the analysis taught them that they have a significant impact on the Baseline.

Baseline annual evolution

Baseline annual evolution
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challenge
2

Consumer goods: when to stop doing advertising

If there were no budget constraints, and there were no seasonal moments negatively affecting sales, the recommendation for TV advertising investment would be to be on air the 52 weeks of the year.

A client from the consumer goods industry, used to having a continued presence in the media, asks for our help to assess when it was least damaging to stop investing in TV.

Solution:

First, the impact of television on sales in the short and long term was measured and for how long its effect lasted when the campaign was stopped (ad-stock).

In addition, we examined which were the weeks on which every year a reduced demand of the product was observed (negative seasonality).

Actions carried out:

With all of this, along with further analysis to assess the effectiveness by month, taking into account the costs of each, different scenarios were created and it was evaluated in which one of them more sales with less cost were obtained.

Results:

The final recommendation was that the optimal strategy was to stop investing during the negative seasonal periods. In the case of having to make breaks interspersed continuously throughout the year, one week was considered better than two.

Consumer goods: when to stop doing advertising
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challenge
3

Mass consumption: brand value

Why does a brand cost more than another offering the same product?

The answer lies in the added value that brands give to products as perceived by consumers themselves. Today brands offer consumers an experience that exceeds the initial functions that products had before.

For this reason it is important to quantify it and see it positioned with respect to competitors.

Solution:

We collected data through a CAWI survey (Computer-Assisted Web Interviewing) of consumers in the selected category.

Through the questionnaire, a number of different pricing scenarios were proposed to respondents, allowing us to know what would be the brand of choice in the different contexts.

Additionally, questions related to brand awareness, advertising recall, brand preference, image and valuation of each of the brands analyzed were included.

Actions carried out:

We combined subjective results (brand health) with objective results (market share) to quantitatively measure the strengths and weaknesses of each brand.

We obtained accurate pricing curves that estimate the market share of each brand based on the fixed prices.

Results:

With this process of brand valuation we identify and evaluate which are the differentiating drivers of each brand

Brand valuation is useful to the company because it helps to know the current value of the brand and that of competing brands.

The results obtained allow us to also obtain the optimal price for each analyzed brand, i.e., the price that customers are willing to pay for each one of them.

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challenge
4

Consumer goods: pricing analysis

Their objectives: Study the price elasticity of two of their brands in the same product category being analyzed.

Define pricing recommendations for the two brands considered together or individually.

Solution:

We designed a hall test type survey in which we analyzed:

  1. The probability of brand switching vs. the price of the brand in the category.
  2. Choice of a brand within different price scenarios (price sensitivity).

Actions carried out:

With the results of the hall test we performed a consumer behavior analysis vs. brands price change. From this analysis, for each brand we built an indicator of probability of switching and analyzed its behavior against sales.

Results:

We detected the critical levels of change against the price of the analyzed brands.

We achieved an optimal price combination, improving sales revenue of both brands.

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challenge
5

Mass market: evolution of sell in by region

Our client wished to know how sales to the trade have evolved by region (the raw data available are not reliable).

Solution:

Through econometric models, we identified and quantified the main drivers affecting product sell in, both nationally and in each of the regions.

Actions carried out:

Using the variation of sell in and of the main drivers, plus the weights of each of them, we were able to estimate the variations of sell in by region.

Results:

A tool was created in order to calculate automatically the variations by region taking into account the main drivers of sales. Furthermore, results can be updated periodically.

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challenge
6

Mass market: Optimal Price and Market Share

For competitive mass markets, price is one of the most important levers and one that needs to be tightly controlled.

Using pricing analysis we aimed to study the market pricing strategies of a particular product in order to measure its elasticities and, analyze the effects that generate changes in price of this particular product and its competitors.

Solution:

Market share models were used, capable of identifying the transfers between products of the same category and detecting those more

related among themselves. This way, we could measure the variation of the market share in advance of possible changes in price

Actions carried out:

With all the information extracted, a similarity matrix was built, in which we could measure:

  1. How the changes in the product price affected the rest of the varieties in the market.
  2. How the variations of the price of the competitors affected our product.

Results:

With the models and a tool developed in R, we built a simulator capable of evaluating different alternatives of prices and their effect on the Market Shares of the products.

In addition, it was possible to identify the price range within which the product price should be kept in order to generate higher profits for the company.

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challenge
7

Mass market: Influence of online advertising on sales

Our client wanted to know how online advertising influences sales of seven mass market products, divided into two categories, allowing them to perform a media mix optimization in order to reach the maximum sales possible.

Also, we were asked to detect if there was any halo effect among products or categories or synergy between offline and online media.

Solution:

We identified the consumer purchase process and the main drivers for each step.

We measured the direct effect of media advertising on sales, as well as the indirect effect through Queries and Paid Search.

Actions carried out:

Ten econometric models were developed: 7 for the sales of each product, 2 for Paid Search clicks for each category and 1 for the brand Queries.

As a result, synergy between offline and online media was identified.

Results:

We optimized the budget to reallocate media investment by category.

Different scenarios were defined by category in order to maximize sales, depending on the total ad spend available.

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