The data-driven organization

For a long time, people have been talking about creating a data-driven organization, and many of the world’s biggest brands have successfully leveraged data as a way of differentiating themselves from the competition. But is it enough in today’s rapidly changing environment?

One of the most important things I learned in my MIB was that the answer to most questions in life is “it depends.” This question is no different. To differentiate yourself through data and be truly data-driven in meeting with the competition, your organization needs capabilities and data your competitors don’t have. The definition of a ‘data-driven’ organization is being pushed further, with more and more companies leveraging data to make better decisions.

So when am I data-driven?

You are truly data-driven when you manage to put insight into decisions, and make those decisions actionable.

Five stages of data in organizations. Source: Smartsheet

Why being data-driven?

The most data-driven companies are 6% more profitable and 4% more productive than the average company with all else made equal.
83% of businesses asked by The Economist stated that data made existing products or services more profitable.
Companies that use extensive customer analytics have 9x as loyal customers, 6,5x higher customer retention, and 18,8x customer profitability than those who don’t use customer analytics.
But most interesting is that the perceived value of the service or product is 15x higher for those who use extensive customer analytics.

Using customer analytics can actually increase the value of your product or service in the eyes of the customer, enabling you to take a higher price. It makes it easier to understand how big corporations have been disrupted in the last years by start-ups that have used data to create services that are of true value to the customer and creating good customer experiences.

Aren’t we already data-driven?

Many companies believe they are data-driven when they look at their Google Analytics or CRM to check out how they perform. The truth is that this is the first and most basic step in becoming data-driven.

To be considered a data-driven organization today, and experience differentiation compared to the crowd, you should be able to follow through on all these four levels:

1. Descriptive Analytics

The purpose of Descriptive Analytics is to summarize all the data you have into understandable insights. This method is used to understand the’ underlying behavior and is the category you end up in when you only look at Google Analytics or similar services. While it is the first step, it does not mean that it’s not useful. Analyzing behavior and engagement is of great help when you create targeted marketing or service improvements, and similar. Visual reporting and data mining are the main areas in this step.

2. Diagnostic Analytics

The purpose of Diagnostic Analytics is to understand why something happens. You use it to figure out the root-causes of events to determine what factors contributed to the outcome. While Descriptive Analytics gave you information that ‘something’ happened - for instance, an increase in sales over a period of time - Diagnostic Analytics will tell you why you experienced that increase in sales. The main areas in this step are What-if-analysis and root-cause Analysis.

3. Predictive Analytics

Predictive Analysis is used to predict future outcomes. This is where you really start to make better decisions than your competitors. It sounds really fancy to be able to predict the future, but there are some limitations. You can’t predict if an event will occur in the future. However, you can predict the probability of the event based on your descriptive analysis.

The core concept is to create models so that existing data is properly understood, and you’ll be able to predict the future based on the knowledge you get from your existing data. In most cases, a company will need dedicated data-scientist and Machine Learning specialists to build these models. The most popular tool to use is Python, R, and Rapidminer. If done correctly, it will get you a step further than standard Business Insider predictions. However, tools like Squark is making Predictive Analysis available to the masses.

4. Prescriptive Analytics

Prescriptive Analytics is what will provide you with a competitive advantage in the time ahead. It goes beyond Predictive Analytics and can suggest future solutions. Prescriptive Analytics suggest all favorable outcomes according to a specific course of action and suggest action courses to get a particular outcome. Prescriptive Analytics actually suggests favorable solutions for you and is the most advanced form of Analytics available to us. Having these capabilities will be essential to be able to compete in the future.

How do I start?

It can sound daunting if you’re starting from scratch for a market organization, but if you have a specific timeline and plan, becoming data-driven is something most organizations can manage if they are willing to allocate resources towards it. And the truth is, if you’re a small company in a market with other small companies, just staying one step ahead of the crowd can be a game-changer. The struggle is often figuring out where to start, if it will bring gains, and avoiding spending all day working with analytics.

But there is a discrepancy in the perceived cost of having a higher level of maturity in Analytics and the reality. Marketers today spend 30-50% of their time, manually handling data. Imagine if much of this is automated. So how do you increase your organization’s Analytics level?

Level 1 - Visibility

Level one is about eliminating those manual data pulls. This is where you unify metrics from all your data-channels and put them in the same source of truth. Today, let’s say you use both Facebook and Google for advertisement; they will both try to take the credit for a conversion on a bigger journey. Gather the data into one channel to experience the true results. Using tools like Adverity, Oribi or Funnel will help you in this step as well.

How-to

In 30-60 days:

Cross-channel dashboards - Identify all data sources in your ecosystem.
Source performance dashboards - Aggregate data into a single source of truth.
Data aggregation and unification - Identify the owner of the strategy and analyst resources available.

And start figuring out what tools you need to follow through on your work.

Level 2 - Alignment

This is a step many seem to ignore or forget. It might seem like a small deal, but it is a big one. Aligning marketing and sales. If you wish for your analytics to be accurate, you must integrate both marketing and sales data. Those who manage to integrate these numbers will have a more holistic and truthful sales funnel. With a more accurate and unified funnel, the job of finding areas of improvement will be a piece of cake. The good thing about this step is that it also simplifies cooperation across the functions.

How-to

In 60-90 days:

Unify marketing and sales forecasting - Implement forecasting methods driven by data.
Unify marketing and sales funnel - Integrate all sales/CRM data alongside marketing data.
Create a full Marketing performance funnel - Identify a data engineer that can work beside the analyst.

Level 3 - Execution

If you wish to have a successful data-driven organization, you have to remember that Marketing is more than just guessing. Execution is about tracking performance across channels and optimize spend based on channel attribution value. And with today’s Ad- and Campaign channels, cross-channel campaign attribution is critical to maximizing ROI. Level 3 is about Campaign Pacing and attribution and is where you get into the ‘real’ analytics. Implementing attribution typically give a 15-35% increase in media efficiency and corresponding increases in ROI.

Marketing attribution: The science of determining which touch points the customer interacted with before making a purchase. These touch points involve any branded interaction from a television commercial to a promotional email offer.

How-to

In 60-90 days:

Create multi-channel attribution modeling - Determine the attribution approach and commit to it.
Campaign pacing and optimization - Optimize based on campaign performance across channels.
Campaign planning and tracking - Identify data scientists to work alongside the analytics team.

Level 4 - Prediction

The last level is about predicting future results. Data-driven budget allocations based on Marketing Mix Modeling can drive top-line growth by at least 10%. You predict the future outcomes based on current and past performance, and this is where your analytics differentiates you from the competition today.

How-to

In 90-120 days:

Predictive Media Mix modeling - Drive planning based on media mix modeling scenarios.
Customer journey modeling - Customer journey results to enhance targeting strategies.
CMO drives growth through the results of the analytics team.

Conclusion

So now there’s no going back, and no reason why not starting to make data-driven decisions.