At a recent MarTech event, Theresa Regli with Real Story Group gave her 10 “Myths” that MarTech vendors tell their customers.
CIO recounted those “myths” and Regli makes some great points, but only to the degree that a lot of vendors say they can do things that clearly they can’t. Even when she says it’s impossible to buy a fully-integrated marketing suite (Myth #1), she’s right on one level. Most vendors selling this concept have simply bought up a bunch of tools that aren’t well integrated, creating Frankenclouds that aren’t integrated enough to even allow for single sign-on.
There are, however, a core set of capabilities that should be integrated: data, insight, and action. These core, integrated capabilities wrapped in a bi-directional connectivity layer, allows us to communicate across all channels and enables our clients to communicate with their customers across any channel, inbound or outbound, at any time. While it is true that no single vendor offers a single stack of marketing technology, Redpoint does provide a tightly integrated core and seamless integration into the greater marketing ecosystem that delivers a single point of operational control and a single point of data control. (Myth #3).
But it’s also why vendors that offer what are essentially channel solutions cannot offer to help you with your data warehousing needs (Myth #8). If they are used to dealing with data from the web or email in a list based marketing environment, how can you count on them to handle complex data integration, identity resolution or unstructured data from Twitter, Instagram or even your call center? For that, you need a vendor that focuses on creating a golden record that links data from the physical, eCommerce, Social and Mobile domains of data. Only in this way can a marketer truly understand and connect every interaction between a brand and the customer and execute a precise and coherent strategy.
With this level of data accuracy and connectivity, a marketer can focus on more sophisticated strategies that go beyond the traditional channel-based messaging. Instead of tying the channel to the message, marketers can think about a full customer journey in which the messages the customers receive along the way are delivered consistently, regardless of the channel being used; whether inbound or outbound, digital or traditional, synchronous or asynchronous
With this level of “nirvana” in mind, no one is behind the curve (Myth #2), and no, it’s not likely the technology can be up in “no time”, particularly if the data needs to be integrated. However, Gartner does rate us highly for customer satisfaction (Myth #7), and much of that satisfaction comes from the speed and efficiency with we implement and deploy our solutions for our customers. Still, you can’t sign a check on a Thursday and be executing programs the following Monday (Myth #4). As for the training (Myth #5), of course, training is needed but those are easy mechanics in operating the platform and are trained in less than a week. The more important area to focus on is the strategy and customer experience that the marketer wishes to achieve.
Regli is right about scaling (Myth #6), for most tools it’s true that they don’t scale, but the reason isn’t that they are architected poorly, it’s that they were architected at a time long before social networks, Big Data and IoT. Most of the tools in use today by our competitors were built before common channels like Facebook, Twitter and Instagram even existed. These are tools designed for the dial-up world of AOL, not the always-on world of the iPhone and Facebook. These are architectural limitations that will increasingly affect the performance and scalability of older platforms as the world rambles ever deeper into the IoT.
As for her last myth, that MarTech tools can understand the sentiment, I 100 percent agree with her there. If you feed the same data into two different tools, you often get two very different answers. The truth lies in the dirty work of having your own data scientists with a clear understanding of your business model and context handle sentiment. Of course, they need the right data from which to work, without it, they can’t provide the right insight to take the correct action.
As the old adage goes, if something is too good to be true, it probably is. The same might be said for marketing technology. There are no easy answers for complex data problems, especially when the technologies trying to solve them were architected for the pre-social media world. In those cases, sometimes there are no answers at all.
