Businesses that work on an omnichannel marketing strategy rely on what is called a Single Customer View to monitor customer behavior patterns. Essentially, a Single Customer View (or SCV for short) refers to an aggregated view of a customer that ties in their visits from multiple channels and avoids duplication or dilution of customer data. For instance, SCV ensures that a customer who visits your website to check your products and then calls up your office to place an order gets tracked as one unique customer and not two.
Setting up an SCV is challenging for more than one reason. Firstly, it is not always possible to track down visitor credentials across multiple channels. A customer calling up your sales team could do so from their landline while they sign up on your website with their mobile number. It may hence not always be possible to consolidate these details into a SCV pattern. Secondly, the quality of data extracted from channels like TV or radio could be insufficient to map a clear picture of your customer.
Businesses typically make use of a Data Management Platform (DMP) to handle customer data. This is essentially a data warehouse software that pulls in data from multiple sources and interprets them in such a way that it makes sense for business decision making. In some ways, a DMP is the software that interprets data while SCV is the aggregated storage system holding this information. Bringing these two together is essential for effective data mining.
The poor quality of data however prevents businesses from piecing together a reliable SCV and interpreting it through a DMP. This gap is filled with the help of customer relationship management tools that replace unique rows of SCV with generic customer profiles instead. CRM tools make use of data available with them to identify demographic and behavioral patterns that provide maximum ROI and thus help businesses target the right channel and demographics in their marketing campaigns.
The opportunity to replace an SCV with customer demographic patterns is all the more reliable in the case of retailers who cater to large volumes of customers. Here, the data gleaned from millions of customers may fit one of the several customer demographic buckets that your marketing campaign is looking to target. So while the business may not own a precise SCV for their customers, they may still be able to target campaigns based on customer profile.
You may call this a Single Customer Group View (SCGV) for the lack of a better phrase. Essentially, SCGV makes use of precise SCV information available with the business and extrapolates this data and pattern to fill out the SCV for other customers who have not been mapped completely. This helps achieve two things – one, businesses may either use big data analytics to grow partial-match SCVs into fully mapped rows of information using data available at hand. Alternately, marketers may identify customer behavior patterns with fully-mapped SCVs and use it to create SCGVs that are essentially groups of customers who have similar behavioral patterns.
When it comes to retail analytics, partial data is as good as no data at all. However, with big data analytics and SCV mapping, it is possible to create meaningful information that can then be used for future marketing purposes.