At uPromote we provide a full suite of analytical services to meet your customer management objectives.
Those services are broad and customizable and generally include:
||Development of a scorecard on the health of the customer portfolio, including measures of spending, product penetration and attrition.
||Development of algorithms to score and rank the customer portfolio by income or profit criteria.
||Development of business rules to partition the customer base into groups that are similar in behaviour and demographics.
||Development of predictive models that may be used to optimize targeting and reduce attrition.
Discovery is often the first stage in building a comprehensive customer management plan. The main
purpose is to determine how customers are differentiated by their value and characteristics and to know
precisely how much to invest in cultivating the relationship. Customer relationships are
assets that should be evaluated and managed as rigorously as any financial or physical assets.
Discovery is used by organizations who want to:
||Identify their best customers and treat them differently.
||Understand the current value of individual customers - and their potential.
||Differentiate the way they communicate with customers so that their marketing can be more relevant and persuasive.
||Invest wisely in the right customers to realise a maximum ROI.
||Develop new value propositions that will help to attract and retain high value customers.
By coming to grips with the "economic value" of their customer relationships, organizations
have the opportunity to allocate marketing investments more wisely. Rather than budgets being
spent uniformly across a customer base, marketing and sales efforts are redirected to where
they are most likely to have the biggest payback.
The definition of customer value may vary somewhat by industry and organization, but is
generally defined as actual profit contribution within a fiscal period. Once the distribution
of customer value is known, the next step is to find the key behavioural segments that account
for most of the activity within the portfolio. This involves grouping customers into segments
that share similar characteristics across a range of dimensions, such as spending, geography
and age. The objective is to form meaningful, durable and actionable segments that warrant
Armed with the knowledge gained through the discovery phase, marketers are positioned to
choose how - and how much - they are going to invest in each segment. They can begin to create
the segment-specific offers, programs and communication streams that will maximise customer
The main deliverable of the Discovery phase is a Customer Portfolio Analysis that usually
consists of the following major sections, depending on the organization's requirements,
industry, type and quantity of data available:
Customer measures include all values that are the main profitability drivers. The customer
population is divided into equal groups (i.e. deciles, quartiles, or percentiles) based on a
continuous value, such as total spending. Customers with the highest values will be found in
the top ranked groups, those with the lowest values in the bottom.
The dimensional analysis shows the relationships between key dimensions like customer counts by
product type; by vintage (i.e. customers that share the same join date); and by geography.
Particularly important, so long as the historical data are available, is a retention
analysis showing the number of active retained customers by year within each original group.
The customer base is ranked by profitability (or some proxy like total past year spending) and
divided into equal-sized segments or value tiers. Descriptive statistics are provided - counts,
sums, means, maximum and minimum values - for every performance measure.
Segments are compared to determine differences in characteristics and behavior. By comparing factors
such as product penetration, number of visits, intervals between visits, average annual spending and
average transaction amount, it becomes possible to identify the key determinant variables that shape
the differential value of each segment. This sets the stage for development of a practical, durable
and low maintenance customer classification method (or taxonomy).
Geographic AnalysisCustomer counts and transactional data are aggregated to geography, then
benchmarked against objective data to produce penetration estimates and demographic profiles by area.
Mapping segment distribution often provides a very clear explanation of variable performance.
The analysis is accompanied by an interpretative overlay that highlights the key findings and
observations; explains the business implications and meaning behind the statistics; and
presents a recommended segmentation methodology.
While the Discovery process is useful for strategic planning, marketers are also interested in
optimizing the results of their tactical programs. Predictive modeling can help by assigning a
mathematical probability rule to the likelihood that a customer will respond to a direct marketing
offer or purchase a specific product. By modeling, scoring and ranking customers and tactical tools
only those performing effectively need be pursued, thus improving the efficiency of the marketing
campaign. Models can also be used to calculate the probability of a customer becoming inactive or
to estimate future spending.
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