According to a June 2022 Spotlight report by the IDC, investment in technology to improve data integrity has a positive impact on a wide range of business metrics, with customer satisfaction topping the list of improvements at a significant 42%. Improving the integrity of data requires actionable data intelligence that increases the transparency of data by creating context, and enhances data quality through repeatable processes and by enriching internal data with trusted external sources.
But how does improving data integrity directly affect customer satisfaction?
Break down data silos
The size and complexity of most large organisations means that these businesses frequently break up operations into smaller business units, subsidiaries, or even product/brand focused groups. This makes sense, as smaller units are more agile, easier to measure, and can cope with different operating procedures or systems that may be necessary to drive what may be, in some cases, very different businesses.
However for a customer dealing with this enterprise across a range of business units or product portfolios, this can mean a very disjointed and inefficient experience. A simple example: marketing from one business unit may continue even after the customer has requested marketing to stop from another business area; or a customer may be asked to update the contact or banking information by multiple business areas – leading to an annoying and inconvenient repetition of effort. In the worst-case scenario, a customer with a longstanding and profitable relationship with one business area may be treated poorly by another business area where it does very little business due to a lack of visibility of the customer’s total value to the broader enterprise. This can lead to customers churning their higher value businesses.
What is efficient at a business unit level can be inconvenient, or even create a negative experience for a customer expecting a consistent experience across multiple business units. Breaking down these business silos can help the business to gain a better understanding of each individual’s footprint across the total enterprise. By helping each business unit to understand the customer’s preferences and footprint, the enterprise can ensure a more consistent experience that is aligned to the customer’s preferences. Breaking down silos can also help us to improve how we communicate with customers, particularly as we make the shift from face-to-face interactions to digital channels.
From multichannel to omnichannel
Almost every business, of any size, is now interacting with customers across a range of digital channels, including websites, call centres, mobile channels, social media platforms and on-premises. The COVID-19 pandemic accelerated the adoption of digital platforms, both by consumers unable or unwilling, to travel to traditional stores, and by businesses looking to find new ways to connect.
The speed of adoption, particularly of new channels, can lead to disjointed experiences. For example, a customer may spend some time on a website, interacting with a chatbot, before making the decision to phone a call centre to finalise an order or deal with a problem. In many cases, the history of the previous interaction may be lost, and the customer is forced to repeat the entire call.
Breaking down channel silos moves us from a multichannel capability – one where the customer may choose to interact with a company via any number of channels, but where each interaction is treated on its own merits – to a truly omnichannel capability – one where each interaction is shared for future reference and knowledge is not lost. This was a key opportunity realised by the Norwegian financial institution, DNB Bank as they reinvented themselves as a customer-centric digital bank. DNB approached specialist data integrity vendor, Precisely, for data quality solutions to manage the challenges and opportunities created in the business by the explosion of data brought by digitisation.
According to Aidan Millar, chief data officer at DNB, everyone talks about going digital, but if you are not capitalising on data streams that are generated through your digital channels, then you are going digital without listening. Millar’s role is to leverage digital interaction data to reconnect and stay relevant to DNB’s customers on digital channels.
Of course, this avalanche of data also creates opportunities for data-driven marketing.
Personalisation
Not only do customers not respond to blanket attempts to sell them products that they do not want, but they can also actually drive existing customers away. Conversely, research shows that companies that excel in personalisation generate 40% more revenue from those activities than average players.
According to Morgan Chase, CIO at Lori Beer, “If there is anything that the past year has taught us, with a pivot to distancing and digitisation, it is that personal, tailored experiences really matter − in banking and just about everything else.”
The ability to deliver what a customer wants hinges on the ability to understand the customer in the first place. Data analytics is therefore the key technology for improving customer experience across all touchpoints.
“Data is key,” explains Quinton McKenzie, Sky TV New Zealand’s head of Corporate Core. “It really allows the understanding of your business and your customers, specifically within our industry. Customers want to be talked to personall. The days of sending out blanket emails or comms, or even putting customers into segmented groups are gone.”
As companies push into new ways of using data and creating insights about customers, the real challenge is ensuring that they have high-quality data, especially if they want to leverage data to drive personalisation.
All data is big data
Big data was a term coined by Doug Laney in an effort to describe data that is growing rapidly (Velocity), comes from many sources (Variety), and has high Volumes. Five or six years ago, this may still have meant a subset of data within the enterprise. Today, the thirst for data has grown exponentially, and that means broader datasets, alternative data and deeper history. Almost all data exhibits one or more of these big data characteristics.
According to Spiros Giannaros, president and CEO at State Street’s investment technology firm Charles River Development, current events drive people to look for insights in areas they have not needed to in the past. Whether investment firms are analysing cargo ships stuck in port, satellite imagery of shopping mall parking lots, or consumer sentiment metrics captured on social media, the ability to leverage these new data sources is key to hedging risk and having first mover advantage on investment opportunities.
The old adage of ‘garbage in, garbage out’ still applies, only on a larger scale. Companies must deliver data integrity at scale, across vast data sets and across data landscapes that bridge both on-premises and cloud architectures. This has led firms like State Street to implement new data management solutions, build data lakes, and determine which service providers can accelerate their desired outcome-trusted data to make informed decisions.
© Technews Publishing (Pty) Ltd | All Rights Reserved
printer friendly version