How to avoid CRM ‘death by dodgy data’
Getting good results and ROI from large investments in CRM also demands a solid data quality strategy and even greater investments in data quality technology, according to Nigel Turner, vice president of information strategy for Trillium Software.
There's big demand for CRM as a service, which has been made much more affordable by 'cloud computing'. Businesses are lured in by the promise of unifying the enterprise in its mission to market to, sell to, serve and retain customers, but many seem to forget that good CRM first demands good customer data.
The CRM services market has seen renewed energy and expansion in recent years. Microsoft, Oracle, Salesforce.com, SAP, NetSuite, Sage, SugarCRM and others have been clamouring to offer a hungry market 'cloud based' and 'on-demand' (hosted) CRM systems.
In fact, according to a study by Nucleus Research, 63% of enterprises with more than 1,000 employees have already adopted on-demand CRM technology, while Gartner found that 35% of all CRM applications are provided in the form of SaaS (software as a service) – a figure which is likely to grow to at least 50% by 2020.
The attraction of CRM, of course, is the promise of unifying an entire enterprise, and being able to track, monitor, serve and sell to customers more effectively than ever before. Another survey by Nucleus Research suggested that, on average, every US$1 spent on CRM delivers a US$5.60 return. But that kind of ROI isn't a foregone conclusion by any means.
Data challenges impact satisfaction
Reportedly, at least 25% of most companies' data is probably inaccurate. In fact, according to one well-respected analyst firm, 50% of companies are not satisfied with their current CRM programmes for exactly that reason: a lack of data integrity.
Research by Aberdeen Group showed that, out of of what it calls 'best in class' performers, only 48% of firms are satisfied with their data quality, as are only 36% of others. Research by Ovum also found that bad data is costing US business an estimated US$700 billion a year (about £70 billion for the UK), and that translates into about 30% of the average company's turnover.
Quality is the key to CRM performance
Quite clearly, while an Economist Intelligence Unit (EIU) report commissioned by Capgemini found that 75% of business leaders believe their organisations to be data-driven, that data has to be of high quality. But how can you expect your data-driven CRM investment to perform well if you can't trust the quality of your customer data?
Good customer data involving order history, contact information and preferences means that you have real potential to improve the effectiveness of marketing campaigns and enhance loyalty by ensuring cross-channel relevance to each individual. It also means reduced waste from poorly targeted and duplicated mailings, and protecting customers from becoming annoyed by off-target messages. Good data allows for the identification of new customers that need to be nurtured, and loyal ones for special treatment.
From a service and contact centre perspective, good data means accurate and fast customer recognition, recent transaction and engagement identification, personal service and speedier problem resolution. Reliable data also allows for better compliance with data protection laws, with opt-outs, suppression, do-not-mail and other exclusions.
One of the big challenges of ensuring that your data is of high enough quality for effective customer relationship processes is that it now enters organisations in ever increasing quantities. In the age of Big Data, firms are seeing data volumes grow at a rate averaging 50% a year. On top of that, customers are interacting through an increasing number of contact points, via ever more devices, and in ever more formats.
Single customer, real-time view
Before this huge flood of multi-format, multi-source data can be woven by CRM services or other processes into meaningful intelligence to drive customer relationship and loyalty programme actions, it needs to be cleansed then matched with other relevant data from inbound channels as well as existing legacy data for that customer.
Given the volume, velocity and variety at which data is arriving, the challenge is one of latency. The EIU report suggested that there is a very real need to overcome this challenge, noting that 85% of business leaders said they were experiencing problems due to their inability to analyse and act on data in real time. The same survey also found that 56% felt that organisational silos were the biggest barrier to effective Big Data-driven decision making.
Three steps to achieving CRM-compliant data
The key to ensuring good data for CRM and for loyalty programmes is to create a data quality compliance process. This means ensuring that the data entering corporate systems and processes meets required standards for cleanliness, relevance and timeliness. There are three main data compliance steps:
Assess the quality of existing data and its degree of reliability and consistency for CRM processing. Get some quantified insight into the quality of your data before you even begin to move it to your CRM platform. Data profiling enables you to fully understand the issues in your data and determine what steps need to be taken to remedy them. Specialist data quality software automates this process, enabling you to incorporate your own data validation rules, both for quality and for relevance to your specific CRM needs.
The appropriate data quality software should also convert these rules into processes that transform and correct the data into a common format. A standardised and corrected customer record ensures it will match with associated data coming via other channels and with legacy systems of data collection. This ensures that associated customer, financial, product, and historical data is linked to the correct person as well as any external data appends.
Finally, the same repeatable process created for step 2 can also be embedded into your CRM, Single Customer View (SCV) and other relevant offline and online customer-centric platforms to automate the validation and correction of data at point of capture. CRM users, loyalty teams and others will all have a high level of data consistency, quality and reliability serving their specific business requirements without the latency and cost problems commonly associated with post-CRM data reconciliation.
So yes, embrace the customer relationship and loyalty opportunities presented by data, by Big Data, and by the ease of access to on-demand CRM – but also recognise the need to first have a data quality strategy in place. For all the value data insight can bring, inaccurate data can frustrate and undermine even the best-willed customer relationship efforts.