Consumer Analytics, Loyalty Analytics

Click on any of the following solutions for demonstrations of the wide range of analysis that fall under consumer analytics.

Consumer analytics is one of the dazzling facets of marketing science. Pertaining to the analysis of continuous, individual/household level (customer level) behavioural data, it yields insights that are particularly useful in identifying and addressing business issues.

The data may be sourced from online or offline consumer transactions or interactions, or from managed sources such as consumer panels or loyalty panels.

These structured data sources are ideal for diagnosing the buying behaviour of products and services, especially where repeat purchasing is the norm, through a wide repertoire of analytic metrics and techniques including width and depth of purchase, buyer groups, profile analysis, behavioural brand loyalty, trial and repeat purchase, overlap analysis, basket analysis, gain–loss and forecasting of new product sales. The metrics use analytic algorithms that filter and drill into the data, to yield insights pertaining to consumers buying behaviour.

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Example (disguised): DBS Black.

The fictitious DBS Black case example exhibited above, illustrates a range of analytic tools used for the analysis and validation of new products. Metrics such as trial, repeat usage rate and buying index are some key indicators of the success of a new product, based on which analysts are able to forecast market share. In this example, since the data source is assumed to be the bank’s transactional data, it provides for an estimate of the contribution the new card is likely to achieve within DBS’ portfolio of credit and debit cards.

Of considerable interest too is the new product’s impact on the bank’s total business, which requires answers to questions such as the ones listed here:

The gain-loss analysis, which reveals the amount of business each product is gaining from its competitors, is particularly useful in providing answers to the above questions. In addition, techniques such as overlap analysis provide a measure of the level of duplication within a group of products.

Brand managers are ultimately interested in enhancing their brand’s performance. They would be keen to execute actions that get more customers to try their new offerings, and get existing customers to use them more often. The behavioural diagnostics derived from transactional data analysis, combine well with information from other sources on attitudes, perceptions and beliefs to yield the insights that these managers are seeking.

For a more detailed demonstration of the diagnostic capabilities of consumer analytics, refer to the techniques, examples and cases covered in the chapter Consumer Analytics and Consumer Panels from the Marketing Analytics guidebook.

Analytics reveals what consumer do, which may indeed be indicative of what they think. However to clearly comprehend their thoughts, we need to rely on other forms of research, and in particular, qualitative and quantitative research.

Consumer behaviour may also be inferred or diagnosed by means of machine learning techniques, data mining, crowd sourcing, optimization techniques, visualization techniques and sophisticated cognitive systems. These techniques work well with unstructured data sets that cannot be processed or analysed by means of the conventional methods that apply to structured data. Such data sets which are collectively referred to today as big data, require new ways of data handling and analysis. Prominent among these technologies are Hadoop and Hadoop-related projects, cloud computing, and cognitive systems.

In case you are interested, these analytic techniques are covered in the chapter Consumer Analytics and Big Data from the Marketing Analytics eGuide.

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Customer behavioural data is the marketer’s gold mine that offers numerous advantages:

  • The potency of the data is largely due to its complexion — continuous, disaggregate. Disaggregate data is far more revealing than aggregate data; therefore better suited for diagnosing consumer behaviour.
  • Size. These databases tend to be large enabling the data analyst to drill down into proportionately small customer segments. In particular, transaction data and big data allow for micro segments, or even personalized marketing (e.g. targeted promotions and communications).
  • Though not always representative of total market, the data is highly actionable. It allows for the execution of tactics at the customer segment level and at location — i.e. store or bank or website, etc.
  • Provides deeper understanding of consumers’ buying behaviour. Whereas aggregate data for instance, can compute metrics like market share, disaggregate data goes a step further revealing breadth and depth of consumption, brand loyalty, switching behaviour, purchase baskets and so on.
  • Reveals not only current and historical behaviour, it can also predict future behaviour. For example, techniques based on disaggregate data can accurately forecast market share of a new product soon after its launch.
  • Estimates growth as well as the sources of growth. (Disaggregate data reveals switching patterns through gain–loss analysis).
  • Uncovers the impact of marketing activities in the current time period as well as in the future. Reveals the long term impact of some of these activities.
  • Reveals most valuable customers.
  • The union of people’s physical and the digital world via data integration, yields valuable insights that help better target consumers.
  • Accurate and complete information provides for greater transparency and accountability.
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Business Intelligence Dashboards

Market Intelligence and Data Visualization
Custom designed, interactive, automated, online dashboards that allow for the integration and visualization of market knowledge from diverse sources, in a manner that makes it easy to access and digest.

Consumer Analytics

Consumer Analytics, Loyalty and Consumer Panels
Analysis of continuous individual/household level (customer level) behavioural data to address business issues.

Retail Tracking, Scan Track

Scan Track and Retail Analytics
Dashboards and analytic solutions for reading/analysing retail audit/scan track data; and techniques for analysis of continuous outlet level transaction and shopper data to address business issues.

Retail Measurement Service - Data Processing and Data Visualization platforms

Retail Measurement Service - Processing and Visualization
Automated data projection, data processing and data visualization system for retail measurement services.


Promotion Evaluation
Analytic techniques/dashboards for evaluating consumer promotions in terms of gains in volume value and profit; estimating discount price elasticity, price cross elasticity, decomposing sales, and applying due-to and what-if analysis.


Market Mix Modelling
Assess impact of the elements of the mix, on sales and ROI.

customer satisfaction research

Quantitative Research, Customer Satisfaction Research
Custom designed solutions for data processing and reporting with a working example of an interactive, automated online dashboard for Customer Satisfaction Research.


Ashok Charan,
Marketing Analytics Practitioner’s Guide,
Digital Marketing,
Marketing Mind,
Destiny Marketing Simulator,
Analytics Solutions: Contact by e-mail

View Ashok’s LinkedIn profile