Improving Your Data Driven Marketing Requires Better Data Management

All marketing should be data driven, especially Digital Marketing. When everything can be measured, tracked and attributed there is no reason anything should be done on ‘gut-feel’ alone. Marketing Automation is fundamentally a data-driven initiative. Greater accuracy and clarity relies on better data management and manipulation. Once your Marketing Automation is up and running you’ll have all the data you could ever need and you should put it to good use. Measure the reaction to your actions, analyse what the data tells you and improve for next time. 

Too much data can be just as much of a problem as too little if your Data Management processes aren’t up to scratch. If you’re unsure what you should be measuring and how each metric relates to your goals then the constant stream of data is next to useless. Being able to handle the vasts amount of data you will accumulate and turn it into actionable insights is fundamental to Marketing Automation success. Marketing Automation can only return the incredible results we regularly see reported when it’s insights are used to personalise your campaigns. Marketing Automation without personalisation is like Content Marketing with no new content. It will still work to an extent but not nearly as well as it could.

Better Data Management Marketing Automation


You Have Quantity, Quality Is Down To You

You should never be wanting for data if you have all of the reporting correctly set up in your Marketing Automation suite. Not every metric is useful to you all the time. It is down to you as a marketer to create quality data out of the vast quantity you have at your finger tips.

Quality Data is RelevantAccurate and Understandable.

Work backwards from your KPIs to find metrics and data that are relevant to you. Really drill down and try to find every useful piece of data. Once you’ve found the obvious ones think about what could affect them and whether you can measure that. Whilst you want to cut away any irrelevant data you want to gather as much relevant data as possible.

Once you have found the metrics and behaviours you will measure it’s important to give them context. This can be achieved by goal setting and benchmarking. If a metric is being measured because it affects another metric that contributes to the success of your KPI it may not be clear what ‘good,’ ‘bad’ and ‘normal’ are for this piece of data.Without benchmarks and thresholds it can quickly become overwhelming trying to track dozens of disparate metrics.

There are three distinct types of data you will collect on a customer;

  • Identity – Age, Sex, Occupation, Social Media Account information etc. Properties the Customer has.
  • Behavioural – Clicks, Communication, Downloads etc. The actions the customer takes.
  • Volunteered – Opinions, Motivations, History etc. Information that requires more than a binary answer.

Seeing how these three types of data fit together often presents a challenge. Much of the time you won’t have a complete picture. This is why it’s important to process and analyse data so that you can observe trends rather than trying to understand the motivations of individual customers.


Valid Insights Can Only Be Drawn From Accurate Data

Full spectrum analytics are great but collating data from multiple different systems can be a real headache. Differing data formats, nomenclature and levels of detail open the door to data loss and inaccuracy. If you don’t use a single fully-integrated platform then it is vital that you put together procedures and processes for all data transfers.

Tiny mistakes or incompatibilities quickly snowball into complex problems when thousands of pieces of information are merging every day. Where possible you should attempt to have cross-channel standardisation of formats and layouts. Just as you had to sit down and map out all of the metrics that affect your KPIs now you must sit down and map out your data gathering process. Pay extra attention to data transfers and format transitions. The aim is to identify any areas where data could be lost or malformed and put in place procedures to stop that from happening.

Better Data Management Marketing Automation

The importance of accuracy isn’t confined to getting a customer’s name and address correct. Proper attribution of data and tracking of customers is required to report success or failure. Simply looking at ‘Leads Generated’ and ‘Deals Closed’ for May without any greater context is of little value. Marketing Automation makes end-to-end tracking easier than ever. Rather than simply looking at the number of ‘Deals Closed’ in May you should be looking at where those deals came from. If the Customer became a Lead in January does his conversion in May mean that January’s conversion KPI has now been achieved?

If you think in ‘Snapshots’ rather than the end-to-end process you will never get the maximum returns or most valuable insights possible from Marketing Automation.

Comprehensive and Clear

Although you will be measuring dozens of metrics and data points to come to your conclusions it is not necessary to report all of this data to stake-holders. You need to find a way to report your data and findings that explains you recommendations/actions without confusing colleagues unfamiliar with your Marketing Automation platform. The level of detail and style of reporting is something every marketer has to tailor to their situation.

Once you have decided on a level of depth and format commit this to a process. People unfamiliar with your area of expertise will appreciate parity in your reporting. Try to anticipate questions and cross-examinations and ensure you have any relevant data available. Whatever level of depth you decide to report to it’s recommended to have the data from the layer below ready to present. This is presenting comprehensive data.

To achieve clarity in reporting you should continue the practice of creating processes and where possible keeping standard formatting. The biggest pitfall when it comes to reporting data is assuming because something make sense to you that it will make sense to your boss. What appears logical to one mind may not to another. Consult with colleagues where possible, if they can understand your data without requiring explanation from you then you know that data is well reported.

The final aspect of clarity in reporting is to be honest about the shortcomings of your data. Highlight where assumptions and estimates have been made. Pointing out avenues of required research or where data losses have occurred gives stake-holders one less thing to try and work out.


Action Your Insights To Continue Collecting Data

It’s easy to begin to view customers as nothing more than packets of data, especially when there are thousands and thousands of them interacting with you every day. This is a big mistake. If you don’t continue to provide your customers with value their relationship with the business will deteriorate. Poor content and communication will result in unsubscribes and lost opportunities.

Action the insights you gain from your Marketing Automation data. Track what kinds of content garner the most interactions. What format of eMail gets the most clicks, optimum times of day for communication and the frequency at which communication with customers gets the best response. Don’t be afraid to target your content and let your customer know why you’ve done it. If it’s done correctly telling a customer things about them can be a highly effective way to nurture them and show that you care about giving them a personalised service.


The Three P’s Of Better Data Management


  1. Parity – Across all platforms data and format must be standardised
  2. Process – Every aspect of Data Management should be done via process to ensure repeatable accuracy
  3. Presentation – Clear and logical, raw data and reports should be understandable without you


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