Kenneth Wagner, the Director of Marketing Cloud Services for EMEA North, shares the three common pitfalls for companies, how to avoid them, and why data points and designing to scale are essential.
Data points and designing to scale are critical when working to avoid the most common pitfalls for companies. For context, a data point (or observation) is an individual unit of information. In statistics or analytical content, data points are typically obtained from research or a measurement and can be represented graphically and/or numerically.
Generally, companies aim to grow revenues, reduce costs, or change their operating model. They do this by embarking on a transformation. In marketing, many seek to increase the ability to pick up data points, react, and take the right action. The goal is to create value for a specific person in a specific moment.
Building execution capabilities that match customers’ expectations has become even more important. Together it brings greater revenues, lower costs, and higher satisfaction scores. Additionally, it brings with it an organisation that is ready to adapt to the future.
On the path to success, I’ve noticed that there are three common pitfalls for companies. This blog suggests three ways you can avoid common pitfalls, collect insightful data points, and adequately scale your business.
There’s always somebody who decides on the technology or tools available to the team. These tools are essential for the marketing and digital teams. They are what enables them to deliver the needed business results. But, if the exec sponsor signs the license contract and then loses interest, from what I’ve seen, it’s a bad omen. In many journeys, it has signaled a tougher journey ahead.
Time to understand the path to deliver targeted outcomes from the technology
Willing to champion, network, influence, and escalate the importance of the project
A regular meeting cadence to ensure journey trajectory is accurate (especially early on)
An active and engaged sponsor will ensure a lot less friction; they are essential to delivering a quicker time to value.
Every so often, I engage with customers that are stuck on their journey. For the majority of them, the root cause of why they are stuck falls back to the foundational design.
A common goal for many is to go deliver a quick MVP (Minimum Viable Product). But what I found was that often they had failed to envision their end state. Failing to do that, often ended up slowing and sometimes stopping their transformation journey. They had to go back and fix the foundation before they could move ahead.
Now, there’s nothing wrong with the MVP-based approach. In fact, at Salesforce we champion benefits realisation as early as possible. But it’s important to start envisioning and making assumptions about the end state when you start setting up the platform. This must happen from the start, so we make sure we know what we’re building towards. That is also why it is such an integral part of our method.
Scale: How many countries, brands, and/or departments does this need to support?
Security: Are there any legal, competitive, and/or compliance constraints related to it?
Sophistication: What’s the maturity of the various units and how do you envision they’ll be working in the tool?
Sourcing: Who is going to be the experts? Are you insourcing, outsourcing, or building a hybrid model?
Rapid multi-brand/geo deployments or local autonomy?
Deliver the exact right experience or ensure easy maintenance?
Do you know the size and structure of the centre of excellence?
As with any new technology, you don’t know what you don’t know. To mitigate, we recommend bringing in people with the right experience. They are essential in building the right foundation for your journey. Still, to help reduce the risk of building the wrong foundation, it’s important that you:
Map out the future state by formulating a vision and an ambitious end state
Make sure your team enables themselves on the new technology
Increase the ability to ask the right questions and steer towards the right outcomes
Understanding what makes a good data strategy and when you have enough data points is no easy feat. Beyond that, it is very industry and sometimes company specific. For some, solving basic attribution and personalisation on the website is a massive leap. For others, they’re looking into Amazon and Netflix-level recommendations and personalisation.
As you move up the data maturity curve, you improve your ability to execute and the ability to analyse. You make it possible to get key insights and create the right experiences for your customers. This inevitably ends up as an influencing factor of your targeted KPIs.
What do I know about my customers and prospects?
What do I need to know about them that I don’t know?
What is the strategy and tactics to gather that information?
What places can I collect the data points I’m looking for?
What is the value of the individual data points (i.e. what are we willing to offer in exchange)?
Delivering the right targeted, personalised message and experience
Empower AI models to identify trends and insights
Create more precise segments for targeted initiatives
The common theme? You need more data points and to capture more signals. Without it, you won't be able to deliver the above capabilities. Still, many are stuck in the “permission and transaction” paradigm. They communicate because they have permission. Most fail to focus on humanising the approach and build relationships at scale. You can find many insights on how to shift the approach in the book, Context Marketing Revolution. It is a recommended read for anyone who’s in the digital domain.
In short, engage your exec sponsor from the start. Create a vision for scale that includes your sponsors' buy-in. Finally, figure out what you know, what you need to find out, and how the data points can help you drive results.
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