In the past several weeks, I’ve had more conversations about omni-channel marketing than I thought I’d have over an entire year. There is intense interest in the concept. Omni-channel marketing advocates a seamless pivoting from one marketing channel to the next with an emphasis on the customer relationship. It is a natural evolution of the multi-channel marketing concept but with more attention paid to personalization of messages and enticements. From a customer perspective, interactions flow naturally without regard for the channels they use. In omni-channel marketing, a customer may begin engaging with a brand on social media, then again through the website, and later yet through a mobile interaction and finally to a brink-and-mortar retail store or online merchant for final purchase. Along the way, information and recommendations are provided based on the perceived signals from the customer and aggregate information about other similar customers. This flow of interactions leading to a purchase is often called the purchase journey or customer journey.
Omni-channel marketing is very data driven. It requires tremendous amounts of information about customers, products, and marketing channels – digital, social, broadcast, and print. There needs to be a clear understanding of what a customer wants (or thinks they want), how they wish to engage with the company, and what the triggers are that move them toward a purchase. This requires both individualized data and aggregate data. Individual data helps to detect and interpret signals from customers about the next step in the journey toward a purchase and aggregate data allows the brand to make recommendations to keep that journey moving apace.
Tools such as Informatica’s PIM 7.1 give a coordinated view of products and the signals product data produces. Social and digital media analytics from a range of companies including Salesforce.com, Adobe, and Crimson Hexagon detect signals in social and digital media. Needless to say, there are a lot of software products that help marketing professionals to manage interactions with groups of customers who have similar patterns of behavior.
There are also tools that help understand the individual customer. The average sales automation, point of sale, CRM, and ecommerce system tracks all types of individual customer interactions, especially transactions. Marketing automation products ranging from Hubspot and Marketo to Oracle Marketing Cloud know a lot about how customers are reacting to offers, digital advertising, and other digital touch points. Service software also tracks individual interactions with the customers and contain a vast trove of information about interactions with a company or brand, including negative interactions.
Herein lies the problems for omni-channel marketing. All of this information that should help to move a customer along on the journey to purchase sits in individual silos. There are often big gaps between aggregate data and individual data. The CRM or marketing automation system may know what channels a customer is engaging with and the social media analytics tools may know how customers of a certain demographic are reacting to a new product release but no single system knows both in real-time. For omni-channel marketing to work, there needs to be an automated and gentle nudging of the customer toward purchase along any channel they may be engaging with at the moment. Even when there is a consolidated view of all data pertaining to the customer purchase journey, it still requires several systems or humans to act on the data. The tools are still fragmented.
Eventually, companies that focus on marketing will begin to create integrated and coordinated systems to manage omni-channel marketing. This is the ultimate goal of most of the “marketing cloud” products (of which there are many) and data integration systems. We’re just not there yet. Sooner rather than later, we will see all of these customer, product, and transaction systems coalesce into systems that manage the entire journey from mild interest to actual buying.
What to do with all that data: #PIM 7.1 offers clear view of products & signals product data produces http://t.co/4RV0NwdJ30 @tompetrocelli
What to do with all that data: #PIM 7.1 offers clear view of products & signals product data produces http://t.co/BKQIIZ52ny…
RT @InformaticaCorp: What to do with all that data: #PIM 7.1 offers clear view of products & signals product data produces http://t.co/4RV0…
What to do with all that data: #PIM 7.1 offers clear view of products & signals product data produces http://t.co/Wi3zI3v0yc
RT @AxelTroike: What to Do With All That (Marketing) Data http://t.co/A7nND4k1Bd by @tompetrocelli via @Neuralytix #BigData #MDM
What to Do With All That (#Marketing) Data #omnichannel http://t.co/LdhDZlKc8t
RT @Ohio_Digital: What to Do With All That (#Marketing) Data #omnichannel http://t.co/LdhDZlKc8t
Great point. You are right there is a lot talk on getting “…see all of these customer, product, and transaction systems coalesce into systems that manage the entire journey from mild interest to actual buying”. Aggregating the 360 view of the customer, deciding for a trusted supplier and delivering the relevant product will always have the need to syndicate and match information. The key will be to add the right intellgence on top of these data to make it useable for business people in their apps.