Organisations struggling with new technology isn’t a new problem, and not all hope is lost. There are quick ways to get your foot through the door using data intelligence and influencing process-driven innovation.

Context, context, context

When was the last time you’ve seen a design failure when taken out of context? (See here for examples). It’s easy to laugh it off when the contextual error is purely aesthetics, but if a misinterpretation of data results in your sales teams engaging prospects with an ineffective sales pitch and the wrong product, coupled with an irrelevant marketing program, the impact can be far worse.

Forrester listed three prerequisites that drive predictive marketing success in a 2016 white paper. The first is to refresh capabilities to boost insights-driven engagement, and second is to ensure sales and marketing alignment through lead processes. Last is to gather useful data to inform predictive algorithms.

What these points summarise is the state of needing different pieces in the bigger picture to get the customer view right. With shiny new toys like data automation and predictive analytics in place, we need first to ensure that the people using these data know what to do with it, how it is used—in short, to improve on business intelligence, starting from within the organisation. It is also akin to applying an internal marketing funnel to bring perspective to a process.

There are many ways for businesses to improve their intelligence, and they probably have some areas of the organisation that has developed such skills and processes. The challenge is to combine a very broad view of context within a specific time frame—let’s say a market segment and the products and campaigns targeted toward it―with a multitude of sharply focused views of context, often in real time (such as the interactions and experience provided to a customer).

How can your business reconcile those broad and focused views to define the best decisions and actions? Your stakeholders, be it sales, marketing, or customer experience, needs to apply the same lenses as you would an external customer, to comprehend how data is accessed and more importantly, in what context. Then you need to combine all of their intelligence and related capabilities so you can connect their intelligence.

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People first, always

We have already identified sales and marketing as the most active users of data within an organisation, but this is based on the functional needs of their roles. Similar to how marketers use personas to help us find our best customers look-alikes, a lens can be applied to help us understand what a salesperson or a marketer needs then apply that to a defined lead generation workflow and map that to success indicators to identify what works and what doesn’t.

As Aberdeen Group pointed out, “strongly aligned marketing and sales teams are 53% more likely to ensure relevant value propositions aligned to buyers’ business challenges”. The idea of a strongly aligned team refers to having a set of shared objectives, priorities, strategic and tactical operational processes, goals, and resources.

Understanding what a salesperson versus a marketer needs, then putting them together into a shared program not only accelerates the alignment process between these two functions but also helps the analytics team build dashboards efficiently. We can also apply a particular set of goals tied to these different roles, and further customised the data output according to the actual user needs at any given time.

Linear or cyclical?

Once you have initiated an alignment between internal stakeholders and begin to design shared objectives within functions and cross-functionally, a process of continuous realignment and refinement needs to be set in place. How do you decide then if the process should be linear or cyclical?

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One needs to only look at the shared priority of your stakeholders—the customer. While not all organisations have successfully applied measurements of Customer Lifetime Value (CLV), it is essential to a business focusing on becoming customer-centric. Why is CLV important? It tells us many things that can help maximise sales and marketing efforts, such as:

  • The potential worth or contribution your customer can bring
  • How to move your most desired (i.e. more valuable) customers through each stage of the buyer’s journey
  • Which prospects to focus on with your limited resources

This is where your data-driven intelligence comes into play. Using dashboards set up with combined data sources, mapped to your operational perspectives acquired with your “People First” approach, you are now ready to layer this information against a broader framework of calculating your customer lifetime value.

The business can then learn to identify threats or opportunities throughout a customer’s mapped lifetime while refining its predictions of outcomes of the context, and finally, make recommendations on the best action for the business to take. It is also important to note that when you have billions of people, systems, and devices interacting simultaneously, data is arriving in real time from hundreds and thousands of resources—it has a shelf life; its value diminishing over time, so you need to be able to decide and act instantly, too.

And because a customer’s behavior isn’t always linear, your business intelligence shouldn’t be either.

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