Demographic Insights Drive Alcohol Sales
Problem:
A multinational beverage and brewing company grappling with a multiyear downward trend in sales across its alcoholic beverage lines. This decline was happening despite the general dip in the overall alcoholic beverage market, with the company's competitors managing to incrementally increase their revenue and capture a larger market share each year. The company had deployed numerous internal efforts to reverse its decreasing sales, all of which had proved ineffective. Steven was brought onto the project to support this initiative, hoping to pinpoint the root cause of their deteriorating sales. Steven's role was to leverage his expertise in data science methodologies to create data-driven strategies and generate insights to assist the company in increasing sales and regaining their lost market share.
Solution:
To address this problem, Steven started by working closely with the company's leaders to analyze historical sales and consumer data. As part of its partnership program, the company controlled its distribution channel, selling beverages to distributors and retailers. This gave the company the responsibility of deciding which beverages were sold and which retailers to sell to.
During his investigation, Steven conducted a series of stakeholder interviews. He found that there was often a mismatch between the company's intended consumer for a beverage line and the actual end-consumer. To validate this discovery and assess its business impact, he built a classification model using third-party demographic data sourced from the U.S. Census and Experian. This model labeled each postal code in the United States according to household characteristics such as average age, income bracket, education level, and marital status.
By cross-analyzing the demographic data with the company's sales data, Steven was able to identify patterns and relationships between beverage sales and demographics of areas where the beverages were selling well. The insights from the model revealed that the actual consumers of the beverages were vastly different from the intended consumers. It also showed that a lack of specific local market information was hindering beverages from reaching their intended audience.
Steven improved his model to provide personalized beverage recommendations for each postal code based on its demographic profile. This strategy aimed at ensuring that the company's beverages reached their intended consumers, thus potentially boosting sales.
In the final phase of the solution, Steven collaborated with stakeholders to align internal initiatives with the outputs of his model. The goal was to use the demographic analysis and recommendation model to drive sales and inform product positioning discussions. This combination of strategic market approaches, sales techniques, and data-driven insights led to a rapid increase in sales, particularly in U.S. suburbs, where they witnessed a 6.5% growth. Thus, by identifying the gap in the company's understanding of their consumers and implementing a data-driven solution, Steven managed to reverse the company's declining sales trend.
Demographic Insights – The More Granular the Better
Steven's beverage recommendation model was designed to use historical sales data and product attributes to ascertain the types of beverages purchased at various retail locations throughout the nation. The model was originally designed at a postal code level, providing insights based on broader geographical areas. However, during the analysis, Steven discovered significant demographic variations within neighborhoods sharing the same postal code. These micro-variations were crucial because they signified distinct consumer segments, each with potentially unique beverage preferences.
Recognizing this, Steven adapted his model to operate at a more granular level – the Designated Market Area (DMA). A DMA is a region in the United States where the local television stations in a particular market band together to form an exclusive area. This approach to marketing is based on the premise that a closely located group of people would likely have similar tastes and preferences, often more so than those grouped by postal code.
By refining the model to the DMA level, Steven could offer the company significantly more targeted beverage recommendations, not just by postal code but right down to individual neighborhoods. This hyperlocal approach allowed for more accurate targeting, as the company could now aim its marketing efforts at the specific preferences of each neighborhood.
To maintain its relevance and effectiveness over time, Steven's model incorporated a mechanism to adjust to the changing demographics within the DMAs. Given that demographics evolve over time due to changes in income levels, age distribution, and other factors, the model needed to stay current to remain accurate.
To achieve this, the model was designed to frequently update itself with fresh demographic data from third-party sources like the U.S. Census and Experian. Additionally, it also integrated insights generated internally by the company, possibly derived from its sales, marketing, and consumer research data. This regular updating ensured that the model's outputs would continually reflect the most recent demographic information, allowing the company to consistently target the right beverages to the right consumer groups.
Steven's refined beverage recommendation model combined detailed, neighborhood-level demographic data with dynamic, continuously updating capabilities. The result was a powerful tool that could provide the company with targeted, up-to-date beverage recommendations, enabling them to connect more effectively with their consumer base and boost sales.
Product Recommendations at Scale
The output of Steven's model had significant implications for the company's sales strategy. By identifying which products sold best in which neighborhoods, regional leaders could tailor their marketing and sales efforts more effectively. Instead of relying on broad-brush strategies that treated large areas as monolithic units, they could now target specific neighborhoods with the products most likely to appeal to them, based on the model's recommendations.
The model was integrated into the client's operational environment, meaning it was directly accessible and usable by various departments within the company. Whether it was sales, marketing, product development, or even logistics, different teams could draw insights from the model to inform their specific responsibilities. This broad usage made the model a central pillar of the company's strategy and operations, contributing to its data-driven decision-making culture.
To make the model's output as accessible and actionable as possible, Steven developed an intuitive, user-friendly dashboard. This dashboard presented product recommendations on a DMA-level map, allowing users to visually grasp the geographic distribution of different consumer segments and their beverage preferences.
With the search or selection of a DMA on the map, the dashboard would display the top product recommendations for that DMA, effectively identifying the beverages most likely to succeed in that neighborhood. Moreover, it provided a breakdown of the demographic makeup of the DMA. Users could understand at a glance the average age, income bracket, education level, and marital status of the people residing there.
An interesting feature of the dashboard was its ability to display changes in the DMA's demographics over time. This function enabled users to observe trends and shifts within a neighborhood's population, providing valuable context for the product recommendations. For instance, a neighborhood that was becoming younger might show an increasing preference for certain types of beverages. By showing these demographic trends, the dashboard gave users a dynamic, evolving picture of their target markets, enabling them to continually fine-tune their marketing and sales strategies.
Steven's model and its associated dashboard offered the company a targeted, data-driven approach to boosting sales. By aligning product offerings with neighborhood demographics and continually adapting to changes in those demographics, the company was able to more effectively connect with consumers and regain its market share.