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Join the dots

By aggregating high quality data, marketers can build and maintain a single provider view to optimise marketing RoI

Joined dotsUK pharmaceutical sales and marketing is undergoing a transformation, and product-centric approaches have little traction with today's busy physicians. There is an ongoing shift towards building closer, more interactive and more loyal relationships, leading to higher long-term profitability and competitive advantage.

This business strategy is supported by research from the independent analyst firm Aberdeen Group, which finds that (cross-industry) conversion rates improve by 22 per cent and customer retention by 60 per cent when shifting from segmentation-based email marketing to an individualised customer-focused approach. Additionally, as well as commercial drivers for knowing the customer, there's also compliance with the ABPI Code of Practice to consider. For example, clause 11 of the PMCPA specifies that promotional material should be tailored to its intended audience.

By definition, a customer-oriented approach in pharma requires a keen focus on understanding each prospect and customer; each practice, each institution, each physician and care provider; their inter-relationships, expertise and other profile data. Good customer rapport also requires being fully aware of interaction and transaction histories. Being armed with such intelligence is necessary for accurate analytics, reporting, forecasting, profiling, scoring, segmenting and targeting. Only when these processes are effective might improvements in response rates, cross- and up-sell numbers be won, greater loyalty earned and codes of practice respected.

The importance of an SPV
While the business case for customer-centric marketing is easily explained, in practice making the transformation to and then optimising the technique can be much more difficult.

The cornerstone of any successful transformation to customer-oriented marketing is the ability to build and utilise a single, comprehensive, current and reliable source of intelligence; a single provider view (SPV), or centralised customer data repository.

Building and maintaining an SPV is much harder than it might seem and many companies have been thwarted by the challenge. But without it, pursuing a customer-oriented strategy would require everyone to scrabble around for information. Manual research of customer information or attempts at periodic reconciliations using spreadsheets can involve a lot of re-work, as well as being time-consuming and costly. Ultimately, the use of wrong, incomplete and perhaps outdated information can undermine the customer relationship rather than foster it. The challenge of building an SPV is that there is a considerable amount of data stored in different systems. It's difficult to locate linked information, match it and merge it together accurately.

According to WHO World Health Statistics 2010, there were some 126,126 physicians in the UK in 2009. Large pharma firms typically have proprietary intelligence on hundreds of health organisations, and perhaps tens of thousands of physicians, other healthcare providers, as well as the millions of interactions they have had with them. There's also data from a myriad of external syndicated sources of physician and prescription data. Then there are more direct sources of information, like the physician customer service portals that many pharma companies now offer. Add to this data about responses to different marketing campaigns, online interactions, face-to-face, via inbound call centres and outbound telesales and any transactional information.

With all these sources offering data in disparate formats, the enormity of the challenge starts to become apparent. Consider too that you need an up-to-date view of all this to take greatest advantage — which necessitates reliable real-time data integration.

The data quality challenge
With often hundreds of databases, spreadsheets and data feeds, it can be difficult even to locate the relevant records from which to construct the SPV. Companies are often unable to identify which data is good (accurate, complete, timely, unique and valid), which bad and whether it matters.
The problem is further compounded by not being able to ascertain which records relate to the same physician, practice, Trust or organisation and then match them together accurately; many struggle to spot and remove duplicate, outdated, superseded or generally erroneous records. Such problems can lead to attempts at an SPV delivering an expensive, embarrassing and colossal tangle of largely unreliable and often cryptic data.

Typical problems of working with multi-source data include:
• Lack of standards: differences in spellings of names, common words and an inconsistent use of abbreviations. Incorrect use of fields
• Formatting differences, such as in the way dates or product codes are recorded
• Duplicate data: multiple records (some perhaps outdated) and no way to identify which is correct
• Incorrect data: spurious data entered to overcome an unknown or to avoid its disclosure while allowing, say, completion of a transaction
• Ambiguities: information may be different in different systems — which is correct?
• Rule breaches: data that does not comply with a business rule, such as 'out-of-range' data
• Decayed data: data may be old and likely to have changed.

Data issues such as these will exist even in single sources. If not removed, the problems become exponentially more difficult to manage as sources are consolidated and/or synchronised in the hope of presenting an SPV. Simply investing in a customer relationship management (CRM) system won't provide the answer: without adequate resources to focus on data quality, an organisation's CRM efforts will not be successful.

Getting marketing data right
The good news for marketers is that, as the owners of the data, they can in fact ensure its quality without having to handle the technical detail of the actual cleansing. Here they can collaborate with their IT and data teams. Marketing personnel require a means to be able quickly to identify and isolate the causes of poor quality data and to apply the business rules by which the institution, physician, patient, prescription, drug and other data should comply.

This will underpin underlying analytics, reporting, forecasting, profiling, scoring, segmenting and targeting processes, resulting in improved responses from marketing campaigns. The IT and data teams can then take these rules and apply them within the systems supporting marketing and customer support.

Delivering high quality marketing data is therefore a collaborative process, facilitated by data quality technology that serves the needs of the different departments and roles.

Creating high quality data
In selecting a data quality solution, it is important to establish whether it supports the complete data quality process, from investigation to implementation. The right data quality solution will be one that:
• Enables marketing personnel to visualise, isolate and quantify data quality issues, using easy to use reports and metrics. Personnel should be able to test and store rules that can subsequently be used to cleanse the data
• Applies automated rules-based processes to correct and standardise data. For example, it interprets unstructured data according to context, corrects misfielded and inconsistent data, supplies missing values, standardises values and corrects data to conform to specific business rules
• Consolidates and integrates data from disparate systems. Identifies duplicates and redundancies, links internal records from various business units, subsidiaries, and related companies and defines 'households' for institution-related physicians, creating a single best record that can be traced back to the original source
• Enhances and enriches the data by supplementing records with additional customer information, such as the full postal address, email addresses and phone numbers, preferred method of contact, suppression and third party data feeds, corporate hierarchies and profiles
• Monitors the accuracy, consistency and completeness of data against defined business standards, providing ongoing metrics and trends on data quality compliance for marketing processes.

The benefits to marketing
Applying a data solution to automate the process will not only reduce the time and cost of building a reliable SPV but will also provide a platform for much greater value in the future. The real benefit delivered by high quality data can be expressed in tangible business terms:
• New revenue creation and improved profitability per customer through improved physician targeting, more relevant approaches, higher campaign reactivity rates and improved cross- and up-sell rates
• Improved customer satisfaction, driving long-term customer loyalty and lifetime value, earned through delivering a more responsive, consistent and higher value customer experience across multi-channel touch-points
• Reduced risk of breaching PMCPA guidelines around the distribution of promotional material
• More efficient, connected cross-enterprise business processes through marketing, sales, fulfilment and billing, for example
• Improved sales and marketing staff morale and acceptance of the new system as they find they can trust the data
• Marketing cost savings, by removing the need for frequent data cleansing and reconciliation projects and their demand on manual resources. Reduced mailing/sample returns and time lost through outbound calls to old and wrong numbers
• Cost savings through consolidating outdated legacy systems, databases and spreadsheet-based customer information process
• Decreased integration risks — integrating sales and marketing applications, with downstream support systems, is fraught with risk due to data problems. Good data quality in an SPV and re-usable, repeatable rule-based processes provide a strong foundation for any data integration, whether new sources, systems consolidations or through merger and acquisition
• Faster, more efficient and more confident modelling, forecasting, planning and reporting.

To truly become a 'customer-oriented' pharmaceutical sales and marketing operation requires accurate, complete and up-to-date customer intelligence. This requires that multi-source sales and marketing data is consolidated into a single view and at the heart of this view, there needs to be high quality data. Selecting the right data quality platform is essential, especially for marketing and IT to work collaboratively for an end-to-end, successful and cost-effective outcome.

The Author
Ed Wrazen
is vice president product marketing at data quality solutions firm Trillium Software

To comment on this article, email

    24th November 2010


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