To get value from Data, a company must engage in a process led by marketing. And for one simple reason: getting value from data consists in creating a distinctive competitive advantage that presupposes a good understanding of customer’s expectations.
The first step of a relevant approach to a Data valorization project, whether or not “Big Data”, consists in identifying the data that may contribute to this competitive advantage.
In this first phase – which really focuses on making the inventory of a company data – one essentially mobilizes IT resources. The reinforcement of a marketer and a dataminer is relevant. The scope of analysis is vast as shown in the following table:
| Field | Description | Example |
| Transactional data |
Data captured during standard operations, either from the production IS or marketing IS | Sales records, purchases, payments, CRM data, clicks / emails,… |
| Analytical data | Data derived from transactional data for analytical or reporting purposes | Ratios, calculations, scores, data in DWH and Datamarts |
| “Big data” | User-generated explicit/implicit data and content, sensors-generated data, etc. | Web & mobile logs, sensors, BLE, posts, Likes,… |
In addition, marketers should strive to seek and identify open data which may be relevant in meeting the needs of prospects and customers.
These data are then qualified according to their type from “public” (open data) to “rare” which encompasses data generated by the company’s own information system. (aka “First-party Data” : transactional data, implicit and explicit data provided by website visitors, … ). Data that are “shared” (e.g. with a provider) may constitute an intermediate class.
In a second step, one appreciates the potential of data to create value for the company, globally or on a targeted market segment. Indeed, a differentiated approach may be necessary when the needs of different market segments are heterogeneous. Thus, the potential value of the same data may differ from one segment to another.
The strategic data analysis matrix SDAM®
The strategic data analysis matrix proposes a methodological framework to deal with data valorization projects. It also has the advantage of bringing IT and marketing teams around the data and fostering the emergence of a common “Data” culture.
This matrix classifies the data into four classes.
Each cell gets a type of action to drive:
Junk data
These are public data with low added-value potential for the company, mainly because they are outside the company’s scope.
Dilemma data
These are data produced by the company’s transactional IS, logs generated by website visitors, social networks data, etc. for which the company does not have the keys to valuation.
The recommended action is watchfulness: this data may become of strategic interest in the event of changes in customer’s expectations.
Competed data
These are widely/publicly available data which are already used by players or competitors within the target market. As such, they can hardly help create a competitive advantage for the company as they are not differentiating.
The action to lead consists in finding ways to enrich the data, for instance by merging them with data held by the company. One could also explore ways to valorize them through displaying them in a disruptive way (innovative UI, app, …).
Strategic data
They represent the core of the company’s “Data asset” and they can generate a distinctive competitive advantage. These are the data generated by the company’s activity – therefore “rare” – and identified as having a substantial value potential to the segment. The action to drive in combination with the valorization of these data is their security.
One can get value from strategic data in 5 main areas:
Monetization
The most obvious way to valorize data is to sell them. A site collecting opt-in email addresses can sell them to list-brokers. It may be a short-termist view of leveraging the data asset of the company…
Acquisition / Conversion
The data is used in marketing and sales business processes to help gain new customers or prospects. This approach is rather widely used, eg by social networks that deliver visibility statistics to leverage the conversion of free members into paid customers.
Loyalty
This tactic has a high potential, especially in the B2B sector where data, explicitly and implicitly collected by the company from its customers, can be a source of value-added information for these very clients. New data-driven services can be created, based on the delivery of enriched customer’s data. For example, data helping customers in their decision-making process, industrial statistics that the client can leverage to optimize its management, etc. Through this process, the company is positioned vis-à-vis its client as a supplier of “knowledge” generated from the data collected by the company . To engage in this line of data valorization, it is essential to have a thorough understanding of customer’s business and expectations.
Cost reduction
Some cost-cutting experiments are conducted in the field of supply chain optimization or in the improvement of delivery process where geo-location data are leveraged. Retailers are starting to receive data from routing platforms to refine their pricing, their product portfolio or adapt their level of inventory.
Brand image
This line of indirect valorization, often overlooked, uses the data to leverage company’s branding through data visualization solutions. However, it is quite difficult to measure the financial impact of this approach.
That said, the valorization of data ultimately requires its “activation”. Putting data into action is achieved through the mobilization of marketing technology solutions.
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