Emerton provides Advanced data analytics services and Big Data POC (Proof of Concept) and helps companies designing their data strategies to transform their businesses from top-line to bottom-line.
Recent technologies are making data “the new oil”: thanks to digitization there is always more of them, they are always more diversified, and there are more means and tools to use them. But most of all, innovators are making a better use of data to leverage them as strategic differentiators. The issue is thus not so much about creating more data, but about harnessing them and making sense of them.
Data have always been at the core of Emerton skills: we have built an expertise in finding, collecting, gathering and analyzing data to answer business questions. With up-to-date competences in data ripping, statistics and analytics and with powerful IT tools, we support our clients as they need to step-up their approach to data.
Our client is a large organization serving food growers, their suppliers and clients. The position of the company grants them access to a trove of real-time granular data – from pricing and supply & demand to terrain/weather/growth conditions and statistics – which the client collects but does not utilize.
How can this data be leveraged to improve operations or otherwise monetized?
Data is frequently under-utilized, often because it is siloed within organizations and not well understood. No complete picture of what information was being collected and stored was ever painted and a full mapping of all available data had to be performed.
Data mapping workshops produced ideas around how to best leverage available information and how to combine different datasets for various purposes within the organization. The guiding principles of this analysis phase were feasibility (is the data robust and easy to access/analyze?) and impact (is there strong demand for the resulting information?).
Our client is a large consumer goods company that sells products to hundreds of thousands of diverse on-premise and off-premise retailers.
Emerton was engaged to build and implement an account segmentation engine to align local execution with brand strategies and portfolio objectives (i.e. ensure sales and marketing resources are invested for the right brands in the right accounts). The project required the identification, collection and harmonization of “deep data” from internal and external sources to classify accounts, assign them to relevant segments, and determine their business potential.
Account segments were designed to be utilized by all route-to-market stakeholders, from global brand teams to local field teams, ensuring that decisions be based on comprehensive facts and figures and that all teams work towards common goals. Accounts were assigned to segments based on:
While some structured, quantitative data was available through the company’s CRM, other important data had to be collected and harmonized from external sources, such as distributor data, online provider and social media data, and field surveys.
For example, image criteria (price, critical acclaim and awards, dress code, ambiance, etc.) was gathered from a variety of digital platforms, such as Yelp, Zagat and Foursquare, and consumer typology information was collected from social media networks, such as Facebook and Instagram.
Emerton built dynamic tools to automatically and continuously scrape data from external sources, via APIs, which fed into a back-end analytical engine where algorithms cleansed and harmonized the data into a structured, usable form.
Pilot tests were conducted in select markets to refine and validate the segmentation algorithms. These local checks allowed us to identify any discrepancies between market realities and the automatic segmentation output and adjust the algorithms accordingly.
Classifying hundreds of thousands of retailers and performing in-depth segmentation analysis allowed for the realization of winning account-centric strategies that helps our client:
The segmentation tool was built with dynamic and automatic algorithms, that can be easily updated and re-run, to support long-term strategies where all RTM stakeholders leverage the system to efficiently grow share.