According to Cloudera, the Single Customer View is ‘A holistic real-time view of your individual consumers across all products, systems, devices and interaction channels in order to deliver a consistent, personalised, context specific and relevant experience.’
When we talk about single customer view internally we simplify it to ‘an aggregated representation of all relevant information known to an organisation about their customers.’
And in our Customer 360 infographic we refer to it as the ability to aggregate data from the various touchpoints that a customer uses to engage with a company, purchase products, and receive service and support.
However you define it, the key to the Single Customer View is that all relevant data you have on your customers – whether this is demographic, transactional, behavioural etc - is housed in a single place which can be accessed by anyone who needs to, so that they can get actionable insights to provide a personalised experience for that customer.
Why would I want it?
A single customer view contributes significantly to providing a seamless, consistent and meaningful connection for customers to your brand. You know them better, are better able to provide a relevant omnichannel experience, and thereby drive retention, loyalty and advocacy.
What often happens, however, is that you view your customers in a siloed way because their data is siloed. You know what they buy in store, you know what they contact your support centre for, and you know how they interact with your brand on social media. But that data doesn’t cross over – each part is just a snapshot of the whole picture.
Where you want to get to is an understanding of how each of these snapshots contribute to their entire journey with your organisation. Think of it like pieces of a puzzle. Each piece is a separate interaction or touchpoint, but once all the pieces are put together you finally see the ‘full picture’.
How do I achieve this?
Chances are you’re already collecting a lot of data about your customers that sits in silos in different areas of your organisation. Each shows a different point along the customer journey – and no-one has access to it all. This often causes frustration for customers when they deal with people from different departments that don’t have the right information to provide the most relevant service.
It’s an issue that faces many organisations today – whether through legacy systems, or just how the technology stack was built over time. But, no matter how segregated your data might be there are ways that it can be brought together to help you get your single customer view.
Here’s our six-step guide to help you along:
1. Understand the customer journey and define what data is important to you
You want to identify every touch point a customer has with your organisation and collect relevant data from that touchpoint. Do they interact with your social media accounts, how do they shop (and which products or services do they already purchased), do they call for support? You’ll also need to determine what data is important for you to collect to provide the best possible experience for these consumers. What data can you act on that will enable you to enhance the customer experience?
2. Identify your various data silos Next, you’ll want to find out where all that data sits in your organisation, which data points sits in which silo, who has access to it and what that data tells you about your customer. This will help you identify what is useful, if there are any double-ups, what data you don’t need, and the data gaps you need to fill.
3. Do an audit One of the biggest roadblocks we come across is data that’s ‘dirty’. This refers to data that’s been entered incorrectly - either into the wrong fields, isn’t complete, has spelling or grammar mistakes, or is missing entirely. While this may not seem like a big deal, once you start automating and relying on that data to be correct and consistent, unclean data can really trip you up.
4. Decide on your technology The best place to bring all your data together is in the cloud. While it may be tempting to try and bring it all together into an existing CRM or marketing database, these systems often don’t offer the flexibility that a cloud-based system does. They may have limitations on how the data can be structured and accessed, and likely won’t integrate easily with your other systems for seamless data flows.
A cloud-based data solution however has more flexibility in the structure of data, as well as scalability, accessibility, and integrations with multiple systems to bring it all together, and filter it back out to be used where it’s needed in the organisation. It's a good idea to look into a solution that ingests both structured and unstructured data. This allows for deeper analytics and real-time insights into your customers.
5. Bring it all together In your audit you’ll likely have discovered that your various systems collect customer data in slightly different formats, use slightly different spelling, or populate slightly different field types. You’ll need to match and merge these records to ensure that you don’t duplicate contacts and data. As it’s something that will need to run continuously, this is often done via algorithms using unique identifiers like email address, phone number or credit card details to match those disparate data points to a single person. If you don’t have unique identifiers machine learning can also be used, though a little trickier to get up and running.
6. Feed it back out Now that you have this integrated single customer view – you’ll want to do something with that data. It needs to filter back out to your other systems to create a truly personalised, omnichannel experience.
Now that I’ve got it, how do I use it? You can use the single customer view to provide a seamless, integrated, personalised and - most importantly - consistent customer experience across all touch points that customer has with your organisation.
Sales or retail staff will be able to see previous purchasing patterns – no matter which channel or location this happened at - and be able to make recommendations based on this information.
Automated recommendation engines and next best product suggestions based on previous behaviour and what customers ‘like them’ have done. You can use this data to suggest other products or services relevant to them and their tastes – driving sales and customer satisfaction.
Call centre staff can access all previous interactions with the customer – whether over the phone, through chat functionalities or social media, as well as demographic data and what products and services they have purchased and used in the past. This will help them offer better, more efficient support as they have all the information they need at their fingertips.
Marketers can create targeted and automated marketing programmes, with segmentation based on actual behaviour, interests and purchasing behaviour.
Business decision makers will be able to understand how this all contributes back to business growth, better understand who their audience is, and what their needs and drivers are – so that they can develop improved products or services to meet their needs.
Can you give an example?
For one of our clients, Skinny, we created a single customer view as the starting point for the development of a customer segmentation and churn model. The aggregated data allowed them to understand their customer purchasing and usage behaviour, and with this information segment their database into two groups: local and international customers. They could now offer tailored packages specific to international customers' needs. And when they then went on to work through churn prevention and retention programmes, they could exclude international customers from this marketing activity and focus on the most ‘at-risk’ segments.
The single customer view may feel daunting and unobtainable, but with the emergence of cloud-based software like data warehouses it's becoming easier to achieve. Some would even argue that it’s imperative for organisations, with customers expecting more personalised, seamless, omnichannel shopping experiences – something that can’t be achieved with siloed data.