One of the keys to running smooth operations in marketing is to implement practical data governance. You need to make sure that any data that you are publishing has some standards and rigger to ensure your team is delivering to the internal brand reputation. If you lose trust in the data then you start to have very critical issues within the company.

Step one is to implement the basics, where that if any data is published then you require the basic understanding of the source of data and the time period, and the author. If you put the owner of the data, and when the data was published, when it was updated and where it was sourced from.

Google analytics for example would give you different data to straight awstats logs of hits on website. And you will find depending on the source of data if it is sales from a crm, then salespeople might backdate their sales. So you want to know the period of the data and then when it was updated.

From here you can step into creating a data dictionary, everybody has their own definition of profit and opportunity for example, if you can state the contentious headings and the formula you will find this is gives the transperancy.

People loved how with excel you could click and see the formula. If you can provide that level of discovery in modern stacks like tableau or powerBi you will maintain the trust of the employees.

This is a complete ROI issue, where you need a committee to monitor the level of mistrust, and issues and decide how much the rule need to be enforced to decide the level of data governance.

The next important step in the maturity journey is to look at an approval system. Governance needs to be treated with a mix of training, policing and automation.

Where you can you want to publish your standards, then police what people are doing and then automate the approval. You might have a dashboard with sales and marketing data. You can establish data stewards that approve the dashboard and a ux of data and then a nice workflow for approval for a dashboard that will be company wide. If it is just for the sales department then it may just need one person to approve before publishing.

As with all governance it is about balance and growth and building something while managing your risks.

Data Management Platforms (DMP) are changing and will be able to extend their capabilities to take account of the new technologies introduced by Google in the next couple of years.

There will be a change to analysing aggregating and de-identified data. This is similar to other sources such as Facebook. Google’s intention for the Privacy Sandbox  is to “Create a thriving web ecosystem that is respectful of users and private by default.”

Demand-side platforms, supply-side platforms, customer data platforms (CDPs), onboarders and data marketplaces will increase and thrive in the new environment. All these functions may be absorbed by DMPs.

Which means when deciding on a DPM, there are new questions to asking including:

1. What alternate solution is the DMP providing for the demise of cookies?
2. Does the DMP have look-alike functionality?
3. What are the second and third-party sources used by the DMP?
4. Does the DMP have cross-device identity management allowing stitching profiles across all environments.
5. Does your DMP stitch the user journey?
6. Has the DMP a solution to Wall Gardens? The rise of companies protecting their data has made marketing all that much harder. Even Google, Facebook and Amazon have walled gardens.
7. Is a Data Cleaning Room a feature of the DMP. This will allow a peak into the walled gardens to see how the ads of Google are performing.
8. Match rate testing is essential to enable ROI to be established accurately.

When I go to the bank I want my finance taking care of, but i talk to somoone from personal loans and someone from everyday banking and someone from home loans.

As a marketing and sales team inside a company you often inherit a dispersed company that is structured based on the manufacturing and supply of a product. The first job needs to be unifying this structure under customer needs. The customer need needs to structure the KPIs, and then at some point during the org structure, you need to switch to make supplying cost-effective by unifying who is supplying.

So the person who buys the reinsurance cant help the guy who is delivering credit cards in an organisation. So the reality though is where the flip happens and how the KPIS link up so that products can be bundled and delivered together.

Start with the customer mapping using software like

www.custellence.com

www.uxpressia.com

www.smaply.com

Start with mapping out the customer journey. Next part in the series we will take you through how to build a Center of UX Excellence to encourage a unified vision of customer desires.

3 Step Method for Targets

I will show you a simple 3 step method of creating a target for any team to hit and then how to automate it so your team keeps hitting their targets.

Learnings from a Grocery Company

This came from working with a large grocery company, I mapped the following lists of all the data they were working with. It was 30 systems and each system had up to 240 tables they were meant to be monitoring to get across supply, sales, rois, customer insights, and many other areas of the business they were managing.

The problem with this is with meetings and producing reports and managing the people under him, he wasn’t logging in to all the systems and he wasn’t monitoring what his KPIs were.

I delivered a system for him that allowed him to monitor all his KPIS on mobile and get email updates customised for him and his team. A simple system of management to automatically deliver KPIS.

This utopia seems unattainable and always has been at each company I have delivered for but I always deliver the same three steps.

Step One – Map KPIs

Step one map out each of your initial KPIs you measure and create a threshold for what is red, green, and yellow. It clearly marks what you think is an acceptable level for anything, I suggest even putting in things like customer satisfaction and employee satisfaction and use soft numbers like NPS or a simple survey for employee happiness. It is important to keep in mind you will improve. With one insurance company, I started with 20 metrics and within 8 weeks there were 114. The key is to check the data sources and put your metrics in quickly. Causal mapping is a great technique for this, start with what you want to achieve and then map that back to what will cause that. For example, if you want sales, you need proposals, which need meetings, which need calls.

Step Two – Create a Dashboard

Then run that into a dashboard, with each of the metrics. From here you can then create drill-downs that allow each metric to be investigated:

Step Three – Setup Emails

Now you build out the experience by setting up emails of the main dashboard with clickable links:

And ideally you make a searchable main home page or portal.

From here you can start to make a list of all the employees and which metrics they see and receive.

The Result is Metrics to Manage

You then have the metrics about your team you want to measure, they are being emailed daily/weekly/monthly. From here you put in the meetings to check the numbers and a facility for escalating immediate issues. For example, if you have a situation where a red can cause a problem you can create a trigger for an action committee to be brought together around an emergency to start moving to real-time response to problems.

Workflow Management

Once this is in place I suggest you move to workflow management to respond to these KPIs and data points.

You need to apply basic dataops principles to this, build it quick and simple first in excel, and improve with iterations.

Blueprint

If you need a blueprint please reach out and we can run you through the frameworks for how to make this work. Please email us at mc@trackita.com.