Knowledge is Power
Gathering customer data is one thing. Making it work for you in an effective way to enhance return on investment and further your business is quite another.
At a time when data is the hot topic on everyone's lips, it can often seem that the more information you have, the less you actually know about what matters. Data is knowledge, and knowledge is power - and converting that data into knowledge of customers, and unlocking their spend potential, is a difficult, though crucial, task.
The fractured nature of most client databases, stemming from employing bolt-on "one size fits all" CRM solutions, means pools of valuable data sets are usually isolated and therefore unable to interact to provide the customer intelligence required to drive an effective marketing and communication strategy.
If you want to find out who your most profitable customers are you may find yourself interrogating several systems to add up their spend across each product type. Our database management division has found that generally, the more established the company, the more disparate the data storage facilities. It's a problem worth solving - the more you know about your customers, the better placed you are to maintain their custom, and to sell them additional goods and services.
Key to driving a system rationalisation is getting buy-in across the business - appoint a data steward in each business area, and agree and implement a data governance plan where sponsors are identified and accountable. Rationalising systems to create a single customer view delivers benefits across the three most important challenges marketers face: productivity, customer understanding and financial attribution.
The first piece of the customer intelligence puzzle is assimilating and evaluating your demographic, attitudinal and transactional data sets to build your processes and techniques. By profiling customers and prospects, and identifying the traits of customers that bring greatest value to the business, marketers can target new, high value customers. Equally, by having a full view of every customer's relationship with the business, and the ability to understand the customer life cycle, it is easier to spot and retain lapsed customers quickly, and to build deeper and more loyal business relationships.
Three key steps help to build the knowledge bridge:-
"Still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future"
Because customer segments can be quickly and accurately identified, the time to market for new products is cut. Marketers can concentrate on contacting the right people, at the right time, in the right way, which will eliminate wastage and promotes better customer relations.
Yet still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future. The gap in knowledge between knowing how someone has previously behaved, and predicting how they will behave is massive. The data your company has may be transactional, it may be clean, but it won't necessarily give you the key buying triggers and variables required to drive predictive models. Customers always act within a context, and this constantly changes as new products emerge, events happen, people move. You know some of what they did, but rarely why they did it.
It's likely that in order to examine context and behaviour you will need to supplement existing data sets by matching to third party transactional, demographic and lifestyle data sets such as The Trading Floor's insurance/finance pool.
Identifying trigger data through your previous data interrogation and segmentation work, you will select variables that provide the most value to your data set - anything from date of birth, to occupation, to what newspaper they read on a Tuesday, through to flags indicating a recent house move, their car insurance renewal month and additional contact information.
Complementing your database with additional information and producing qualitative and quantitative segmentation is the cornerstone to driving a customer intelligence strategy, and really understanding why your most valuable customers are buying from you, and how to find more of them.
Pin down your what, why and your when, and you'll not only cement a customer relationship for life, you'll have the biggest competitive advantage. 'Examine. Extrapolate. Enrich' should be the 2008 mantra for the customer-savvy marketer.
(Precision Marketing: Leaders on B2B Marketing - April 2008)
At a time when data is the hot topic on everyone's lips, it can often seem that the more information you have, the less you actually know about what matters. Data is knowledge, and knowledge is power - and converting that data into knowledge of customers, and unlocking their spend potential, is a difficult, though crucial, task.
The fractured nature of most client databases, stemming from employing bolt-on "one size fits all" CRM solutions, means pools of valuable data sets are usually isolated and therefore unable to interact to provide the customer intelligence required to drive an effective marketing and communication strategy.
If you want to find out who your most profitable customers are you may find yourself interrogating several systems to add up their spend across each product type. Our database management division has found that generally, the more established the company, the more disparate the data storage facilities. It's a problem worth solving - the more you know about your customers, the better placed you are to maintain their custom, and to sell them additional goods and services.
Key to driving a system rationalisation is getting buy-in across the business - appoint a data steward in each business area, and agree and implement a data governance plan where sponsors are identified and accountable. Rationalising systems to create a single customer view delivers benefits across the three most important challenges marketers face: productivity, customer understanding and financial attribution.
The first piece of the customer intelligence puzzle is assimilating and evaluating your demographic, attitudinal and transactional data sets to build your processes and techniques. By profiling customers and prospects, and identifying the traits of customers that bring greatest value to the business, marketers can target new, high value customers. Equally, by having a full view of every customer's relationship with the business, and the ability to understand the customer life cycle, it is easier to spot and retain lapsed customers quickly, and to build deeper and more loyal business relationships.
Three key steps help to build the knowledge bridge:-
- Data interrogation - what data you hold on each customer, what information can be deduced from each piece, how it can be harnessed for future sales prospects. Without reliable data, you can do little to build your strategies.
- Segmentation - identifying groups of similar customers. A variety of clustering and profiling techniques can be adopted to identify customers with similar behaviour patterns and demographic trends.
- Hypothesis - once the segmentation is complete and you have robust statistical data sets, with a clear model of the customer life cycle, you can start to generate a variety of predictive models for cross and up selling purposes to existing datasets.
"Still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future"Because customer segments can be quickly and accurately identified, the time to market for new products is cut. Marketers can concentrate on contacting the right people, at the right time, in the right way, which will eliminate wastage and promotes better customer relations.
Yet still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future. The gap in knowledge between knowing how someone has previously behaved, and predicting how they will behave is massive. The data your company has may be transactional, it may be clean, but it won't necessarily give you the key buying triggers and variables required to drive predictive models. Customers always act within a context, and this constantly changes as new products emerge, events happen, people move. You know some of what they did, but rarely why they did it.
It's likely that in order to examine context and behaviour you will need to supplement existing data sets by matching to third party transactional, demographic and lifestyle data sets such as The Trading Floor's insurance/finance pool.
Identifying trigger data through your previous data interrogation and segmentation work, you will select variables that provide the most value to your data set - anything from date of birth, to occupation, to what newspaper they read on a Tuesday, through to flags indicating a recent house move, their car insurance renewal month and additional contact information.
Complementing your database with additional information and producing qualitative and quantitative segmentation is the cornerstone to driving a customer intelligence strategy, and really understanding why your most valuable customers are buying from you, and how to find more of them.
Pin down your what, why and your when, and you'll not only cement a customer relationship for life, you'll have the biggest competitive advantage. 'Examine. Extrapolate. Enrich' should be the 2008 mantra for the customer-savvy marketer.
(Precision Marketing: Leaders on B2B Marketing - April 2008)
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