Big Data — Applications

Nowadays digital data on consumers is ubiquitous, and there are many ways this data is being used to generate valuable insights. Some of the sectors that have progressed most in their use of consumer analytics are listed below:

• Fast moving consumer goods (FMCG) manufacture and retail — brand management, category management, supply chain management, operations. Data sources include — loyalty panels, consumer panels, social graphics, browsing behaviour and geographic information system (GIS) data.
• Digital Media — advertising targeting, website optimization, usage analysis and optimization.
• Conventional Media — advertising targeting, media optimization. (Data sources — media panels, single source data).
• Financial Services — management of brands, products and services; customer relationship management (CRM); credit and risk management.
• Telecommunication service providers — management of brands, products and services; CRM.
• e-Commerce.
• Travel and Leisure.
• Energy and Utilities.
• Gaming/Gambling — segmenting customers and differentiating customer experiences, fraud analysis.
• Government/Public Sector.
• Medicine, Life Sciences — research and discovery based on drug usage analysis.

In general, the applications of consumer analytics serve the objective to enhance customer satisfaction by imparting greater value. They are fairly diverse, though, and vary considerably across industry sectors.

The following examples are intended to illustrate the diversity of applications across two sectors — retailing and credit card services.

Example — Retailer Applications

Loyalty Marketing

Loyalty marketing is the process of utilising customer data to retain and profitably grow existing customers.

It is established on the loyalty paradigm — loyal customers greatly contribute to profits because keeping customers is considerably less expensive than acquiring new ones. The longer they remain loyal, the more they spend and the more profit they generate.

Loyalty marketing is targeted marketing, often through the use of incentives. Statistical analytic techniques are deployed to craft market segments. The typical metrics used include spend per month, frequency of shopping, segments/categories shopped, etc.

When used as a targeted advertising and promotions medium for suppliers, the loyalty panel affords the retailer the opportunity to earn revenue as media owner. Using consumer analytics, retailers and their suppliers can finely tune and target marketing messages and promotions to consumers based on their buying behaviour.

Category Management

Consumer analytics is used in category management in numerous ways to optimize the elements of the retailing mix including use of space, promotions and in-store activities and shelf price. It empowers retailers and their suppliers with insights on how to improve category and brand performance in their stores. (The subject is covered in detail in Chapter Category Management).

House Brand Management

Consumer analytics may be used to improve the health and performance of house brands. Insights gleaned from the analysis of the data help retailers effectively employ house brands to differentiate their banner, generate shopper loyalty and grow their overall business.

Store Management

Consumer analytics may be used to improve store performance, optimize store count and effectively manage a portfolio of stores. Location of stores may be optimized based on the movement and buying behaviour of target customers tracked via geographical information systems (GIS).

Internet Marketing/e-Commerce

Web analytics, the analysis of the browsing and interaction behaviour of internet users, helps retailers engage customers by bringing personalized offers and relevant products to their attention while on site. For example, Netflix and Amazon use algorithm-fuelled recommendations (“Customers who bought this also bought ...”) to drive sales and improve customers’ on-site experience.

Web analytics can help to enhance traffic to website, improve conversion rates and other performance parameters, and evaluate effectiveness of elements of the marketing mix. More information on web analytics is provided in Chapter Web Analytics.

Example — Credit Card Service Applications

Customer Relationship Management

The application areas of customer analytics (including web analytics) in CRM are vast, covering almost every aspect of the CRM process. Broadly speaking, customer analytics empowers issuers with the insights to strengthen their relationship with card holders and increase their card loyalty.

Card Management

Customer analytics can be used very effectively to enhance the performance of cards and improve the management of a portfolio of cards. A typical Card Health Barometer would encompass many analytic-based metrics ranging from usage, spend, demographics and segmentation, loyalty measures, importance measures, financial measures, and a host of special analysis.

Evaluation of Marketing Initiatives

Continuous transaction data helps to effectively track the impact of marketing initiatives over time to provide an assessment of their full impact.

Promotions Evaluation

Market modelling techniques (refer Chapter Marketing mix Modelling) are used to evaluate promotions and improve promo plans.

New Launch Evaluation

Customer analytic techniques can forecast usage of a new card, soon after its launch. The launch can also be assessed in terms of sources of growth, extent it cannibalizes other cards, build-up of loyalty and a host of other metrics. In the context of a portfolio of cards, these measures help assess the full costs and benefits of the new card.

Trade Analysis

Customer analytics based trade analysis helps card issuers understand the potential across trade sectors and retail chains. It helps them position cards within their portfolio so that each has unique strengths across sectors and appeals to distinct customer segments. It helps ensure initiatives are better aligned with merchants’ strengths and weaknesses, and their customers’ preferences.

Reward Programmes

Customer analytics can help evaluate and refine reward programmes so that they are more effective in achieving their goal of increasing card usage and building customer loyalty.