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Customer knowledge analysis

The systematic collection of customer knowledge leads to ability to monitor and perform analysis across the customer base. Monitoring involves developing standardised measures that can be tracked and included in marketing dashboards, while analysis involves using tools like text analytics and statistical analysis or machine learning to look for patterns within the data.

There are normally two types of information gathered in customer knowledge databases, unstructured data such as news reports, or textual feedback, and more formal structured data such as customer tracking, sales and purchasing, questionnaires or other forms of formalised database data.

Approaches to structured information

Companies have structured customer knowledge in the form of sales and transaction databases, contact records, service databases, websites tracking and sales management systems, questionnaires and surveys (which although anonymous, provide an overview of the customer base).

Dashboards

Marketing dashboards provide an ongoing monitor of customer knowledge, pulling together information about new sales, the numbers of new customers, repeat buying, website performance and levels of service calls made and dealt with. The dashboard provides an up-to-the-minute window on the marketplace showing trends and changes in real-time.

Setting up a dashboard system involves linking the structured database data together into a single point of delivery, ensuring that the data is pulled and formatted correctly. This can be combined with identification of key target customer groups and segments to allow monitoring and drill-down to the key strategic targets of the business.

 

Deeper analysis of structured data

Deeper analysis of structured data means looking for patterns and predictors in the data to build propensity models and to identify key groups and classifications. For deeper analysis, a first step is often auditing, cleaning and re-coding the structured data before analysis can take place.


In the past, it was possible to carry out deep analysis using ad hoc statistical tools. However, in modern businesses, the amount of data in the business, and the number of variables available mean that both data handling and pre-processing, and the search for data patterns in the data almost certainly have to be automated and scripted.

Typically, automation looks to find classifiers in the data, that can then be used to drive modelling and predictor variables and from this build models and pictures that help show customer behaviour and improve sales, or reduce defections, or increase connections with customers. Increasingly this is an area where artificial intelligence tools (eg deep neural networks) and more advanced data science tools are being used, together with data visualisation approaches to better explore and characterise the data.

A caution is that statistical models can find relationships, but those relationships need to be validated and tested in real-life among real human customers. Models can show propensities - or likelihoods of action - but customers need to be treated as human beings, as there may be hidden or unforeseen consequences without suitable verification and testing.

In business-to-business markets, customer knowledge may be most appropriate at an individual account level, rather than in aggregate. Key account managers, and service staff will deal with individuals for bids and contracts, consequently customer knowledge is also required right down to the individual contact level, understanding what level of service, contact and sales are required account-by-account.

 

Unstructured and ad hoc data

Unstructured or ad hoc data, by its nature has the potential to be extremely rich in content. It will mostly come in the form of text, audio and person-to-person reports (eg service or account staff), but also through forums, chats and other open discussion formats. The value in ad hoc data comes from uncovering common truths and wider opportunities across the customer base which signal the potential for new products and services.

Text analytics, transcription tools and search systems with the ability to link and group data using data visualisation allow the data to be explored, mapped and interrogated. Text analytics provides large scale grouping of data into themes and sentiments, allowing it to be analysed statistically and presented in charts or on a dashboard.

However, a feature of ad hoc data, is that customers also provide 'good ideas' - insights and suggestions for improvements or new products - and these can be washed out by too much automation - simply machine allocated to a category without being read or seen by a real person.

Consequently, a good customer knowledge system also routes and identifies unusual or new suggestions so they are not hidden in the broad scope of monitoring.

Unstructured data is also essential at a customer-by-customer basis for account and service staff. Who said what, and what is the history of the customer.

 

Experimental approaches

One problem with customer knowledge is that most of the data is backwards looking - it gives the historic picture. Experimental approaches look at how customers will react to changes or new product or services, using measurements in the data stream to test the ideas. These tests, particularly online, can be small scale without affecting the wide customer base and then rolled out when key successes are found.

 

Staff as look-outs

It's also extremely important to realise that customer-facing staff are often identifying patterns and needs among the customers they see, without needing to go to the database. Though these views may be anecdotal, people talking to people may actually provide the best initial insight or guess as to what is going on. So an effective way of leveraging and exploring the ad hoc information your business has is in the form of workshops where customer facing staff are invited to talk about what they think customers want.

Where the ad hoc information does start to reveal useful information, then this is when more formal and structured approaches can be used to assess the value of this data.


For help and advice on building customer, competitor or marketing knowledge systems contact info@dobney.com


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