Data Cleaning is a pre-requisite for an effective Single Customer View. Try our Machine Learning Filters to say good-bye to your Rogue Data now!
A single customer view (SCV) is a data management concept where a single page containing all the information about a particular customer is gathered together so that it can be easily retrieved and reviewed by anyone within your business or organisation.
It is estimated that 16% of businesses in the UK currently have an SCV, in spite of the fact that 93% of organisations in that country believe that to have an SCV for each customer would help them to reduce their costs. A Single Customer View also allows an engagement which is tailored to the needs and desires of each customer while focusses on customer satisfaction. Given the variety of individuals who make up your customer base, this is essential to the success of your company in 2017.
The more you and your employees know about each customer that you have, the more you can tailor your services and products to them and their realities. In a world of big data, you almost certainly have a huge amount of information about each customer, including dark data, but, unless all that information is on one page and thus easily accessible, you are wasting it.
SCVs would allow your rival businesses to treat their customers better than you treat yours if your enterprise doesn't have them, and thus to gain the lion's share of whichever market you are all in. Poor customer service can be catastrophic for your brand reputation. An SCV helps avoid this both by creating the positives of engagement with your customers and by averting the negatives of putting your customers off engaging with your organisation and buying its products and services.
Not that it is easy to create such a set of SCVs for each customer. Indeed without having good data integrity in the first place, it is all but impossible, while an effective SCV in place for each customer is abundant evidence that you now have data quality which you can trust in concerning your customers.
Therefore the best way to start organising your data into a Single Customer View is to pass them through Spotless Data's unique web-based API to ensure all your data have undergone data validation. However, creating data quality is not a one-off affair. Given that the data your company has on its customers is continuously changing, e.g. when customers make new purchases or fill in your feedback form, you should build Spotless into the design of your data lake or use one of Spotless subscription packages to ensure the regular data cleaning of your data. This will ensure that they remain quality data on a permanent basis, giving an accurate and useful picture of each customer you have.
If a customer comes to your business with either a query, or perhaps a complaint, and you have an SCV for all your customers, it is easy for your employee dealing with this particular customer to have all the information your company has about this person at their fingertips. So if your employee can see that this customer has a history of buying your higher quality, more expensive products, they will want to offer them your latest quality offering rather than your re-hashed but more economical product option that is on special offer right now. That bargain deal, though, is ideal for the next customer, based on their unique personal history as expressed through their SCV, of always seeking a special bargain offer or simply the most economical product.
Equally, you should treat a customer with a long history of complaining and demanding their money back with a great deal more caution by your employees than another customer with a long history of positive interactions with your company but who is making their first ever complaint today. The latter customer should be treated carefully and generously, as a failure to respond fairly to their complaint could result in damage to your brand.
However, creating a working SCV for all your customers may be a technical challenge. It may be hard to find a marker to identify a particular customer, e.g. if a husband and wife with very different buying patterns share an email address as well as a telephone number and postal address. Using the customer's name is not ideal, not merely because some people have very common names, such as Jane Smith, but also because newly married women typically change their surname while remaining loyal customers. Even if an effective identification marker, normally a Natural Key, is in place, if the data about various customers are in more than one dataset, platform or sales channel, an accurate SCV may be difficult to create without proper systems integration.
However, an accurate SCV can both give your employees the information they need about any customer as well as creating rules for engagement with that customer. For instance, ensuring that the same customer does not get bombarded with identical letters about your latest campaign, will avoid wasting the time of your customer and your employee and the resources you have allocated to your campaign. If your organization is a charity looking for donations, repeatedly asking a particular customer for help with the same cause may cause her to conclude that you are wasting your resources and therefore should not be the recipient of any donations. Worse still, she may share her frustration at the way your charity works with her large group of friends, perhaps on social media, by saying she thinks the cause is great but the working practices of your charity are so poor that her advice is to donate to a similar charity which seems to do a better job and which, unknown to her, is doing a better job precisely because they have and are efficiently using an Single Customer View for every single customer in their database.
Have a look at our introduction to using our browseable API. You can also test our API on your my filters page though you will need to be logged in first to see this. You can sign-up using your email address, Facebook, Google or GitHub accounts. You can also view our videos on data cleaning an EPG file and data cleaning a genre column which explain how to use our API.
Here is a quick link to our FAQ. You can also check out our range of subscription packages and pricing. Please do sign up for our service using your email address, Facebook, Google or GitHub accounts. We are offering 500Mb of free data cleaning to all new customers so you can try out our solution for yourself. If you would like to contact us you can speak to one of our team by pressing on the white square icon with a smile within a blue circle, which you can find in the bottom right-hand corner of any of the web pages on our site.
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