Improve accuracy = Improve ROI

Effective data management is a key challenge for today’s marketers and data quality is right at the top of their data agenda. Over two thirds of our survey respondents said that they struggle most with accuracy and completeness when trying to achieve better quality data. It seems that consumers are displaying paradoxical behaviours when it comes to supplying companies with their personal information.

The growth in popularity of apps and social sign-ins (which automatically give companies at least a basic level of consumer data) show that customers are happy to trade personal information for entertainment and improved services. Gigya noted in a study of Millennials, that 64% reported using social login because they disliked spending time filling in forms. They also report preferring it because they only need a single password. However, alongside this is the worrying trend of consumers ‘dirtying’ their data. Indeed, in 2012 Cabinet Office security chief, Andy Smith, advised consumers concerned about sharing data to actively provide false details when required to sign in anywhere but trusted sites.

To combat this trend, companies have to engage in a range of behaviours that will increase the confidence of customers to the point where they are willing to share accurate and useful data. In the first instance, data security is key. Publicity surrounding the data breach at internet provider TalkTalk in October 2015 and the subsequent revelation that customers were trying to leave the company shows that there is little tolerance for poor data stewardship. The company’s troubles have been exacerbated by the fact that Ofcom was forced to issue a clear warning about the company’s data stewardship six months previously.

Secondly, companies have to treat customer data wisely, which means only using it for targeted communications that are relevant to the customer. This is where the 69% of brands using basic personalisation fall down. By not adding in consumption, behavioural or attitudinal data, there is a risk of ‘pseudo’ personalisation where the company purports to know the customer (by addressing them by name) and yet sends irrelevant offers or abuses the data by over-messaging.

Finally, to ensure completeness, companies should consider how much data they actually need and how to acquire it. As we’ve already seen, personalisation using the most basic data is largely inadequate, so businesses need to consider how to acquire behavioural and attitudinal information. Fortunately, a lot of this can be acquired using automation or from third parties, rather than relying on the customer to provide it.

One of the more recent advances in multi-channel management comes in the form of the Data Management Platform. DMP’s, which can collate data from a variety of channels and sources, are a way of organising and storing customer data so it can be segmented and tagged to be used in conjunction with other technologies to deliver personalised and targeted messaging without any login requirements. The DMP can also be used with a DemandSide Platform (DSP) to deliver targeted advertising and retargeting, resulting in much more relevant advertising. An Outbrain case study on Seat cars using its Amplify retargeting tool showed a 48% conversion rate increase. Such rates are not guaranteed, however, and retargeting is often not deployed with such success - many consumers report being served ads featuring products they’ve already bought.

Working to reduce errors and inconsistency

Marketing and data automation go a long way to helping the organisation deliver targeted and relevant content. In the multi-channel organisation however, there remain many opportunities for human error to cause problems - 60% of those surveyed for this paper stated that this and consistency were issues for them.

Where customers now operate in the omnichannel, there is real potential for human error and consistency issues to cause data and CRM problems. Retailers are keen to integrate the in-store and online universes, and rely on staff to capture email addresses and mobile numbers to tie together any other online or loyalty scheme-driven activity. If those details are captured inaccurately at point of sale, the connection can be lost or confused.

There are ways to improve human accuracy, such as offering incentives for staff and hurried customers to make sure data is accurate: Both Mothercare and Schuh have begun offering electronic receipts in-store, and the benefits for the customer range from help to maintain accurate financial records to Schuh’s extra incentive of a 365-day returns policy. For the brand, the guarantee of an accurate email address that is linked to an identifiable, quantifiable offline transaction delivers accurate purchase history and contact information in one.

Consistency is not always down to human accuracy however. Brands must be careful how they design their data gathering systems to ensure that each element is feeding in the information required. If a desktop-oriented website is designed to capture 15 data fields, but a mobile version or app is only capturing 8, this is likely to impact future targeting strategies.

Joining the dots

If brands fail to recognise loyal customers in-store or on the phone, the experience is dented. In some cases, when customers deal with differing channels, such as contact centres (perhaps with a complaint or service issue), they can find themselves repeatedly having to provide account numbers, details of problems, or contact information – the brand relationship can be irrevocably damaged.

Encouragingly, the technical ability to prevent such poor experiences does exist within organisations according to our survey. Fewer than a fifth of respondents (18%) believed their IT departments were creating an obstacle to achieving quality data. However, nearly a third (28%) did feel that software tools were making progress difficult.

One of the biggest challenges for marketing leaders in recent years has been understanding the technology landscape - generally and in their own organisations. Many who entered the discipline with a focus on creativity and campaign ideas have been thrust into a position where they must understand how technological systems integrate with customer databases, sales systems and web management.

There can be an internal conflict where the new responsibilities of senior marketers clash with the IT and other departments. Clearly, support from the organisation’s technical leadership is vital in terms of being able to understand the company’s capacity for new or altered technologies to manage data. Marketers have to be able to articulate their data needs now, and in the future they must help their information and technology chiefs make the appropriate decisions. A growing number of consultants, which are neither agency nor technology vendors, are cropping up to help match organisations with the right technologies and to future-proof them against further evolutions in data requirements.

Conclusion

Of course, marketers are witnessing the amount of incoming (or at least available) data increasing exponentially, so need to think carefully about how it might be used to deliver return on marketing investment. As new data streams, including internet-of-things devices come online, the marketers that are able to harness this new asset to shape new products and improve customer experiences will gain the competitive advantage.

Harnessing the data though, is easier said than done. As we’ve covered in this paper; get it wrong and your customer suffers. With this in mind, investing in data quality and making intelligent use of assets, systems and personnel to gain insight and detect problems is absolutely critical.

Next Steps: Data Quality Improvement Assessment

The issue of data quality is quickly moving up the corporate agenda, and yet, many organisations are still not taking an optimised and governed approach towards managing the quality of their data assets

Data lives at the heart of all business operations and an organisation’s ability to unlock its value is critical to ongoing business performance. Where does your business sit in the four stages of data quality?

UNAWARE: These organisations have no concept of the impact of data quality and have not implemented any initiatives to help tackle it. There is a sense of apathy towards the issue of data quality, particularly at senior management level within the business. Business users see data as “good enough” and put workarounds in place where information is sub-standard

REACTIVE: These organisations are starting to react to data quality issues as they impact on business performance but have no specific data-focused roles. They still lack sophistication when dealing with data at a corporatewide level and use tactical point tools within departmental silos for issue resolution. It’s likely that the motives behind any investment in data quality will be in response to a compelling event that has caused the business significant short-term pain (e.g. breach in compliance).

PROACTIVE: These organisations have become more proactive with their data quality efforts. They have started to define roles and create charters that help them to take a more cohesive and unified approach to data management. A better understanding of data processes has begun to break down departmental silos, allowing for collaboration and prioritisation between IT and business users. The organisation is also now likely to be considering the improvement of a broader range of data domains out with customer/party data (e.g. product/financial/location data). They have begun to utilise technology for the data profiling and discovery to help them realise the value of their data assets more clearly and have a more structured process for root cause analysis.

OPTIMISED AND GOVERNED: These organisations have developed a fully governed data quality environment. They can clearly communicate the link between data quality and financial performance to the board. Data has a single owner or entity that is responsible for the maintenance of the corporatewide information management strategy. This would include well communicated and well documented rules, controls and processes for monitoring performance metrics closely. They take a consolidated approach to technology investment, only partnering with vendors that can complement and/or integrate into their existing and established information management practices.

Experian’s complimentary Data Quality Improvement Assessment can help organisations gauge the maturity of their data quality methodology by analysing the three core building blocks of a successful data improvement initiative;

1. People
2. Process
3. Technology

By completing our short assessment, you will understand where your organisation sits on our maturity scale and what steps you can take to improve the quality of your data.

Take your free assessment now at http://www.edq.com/dqassessment