The secret is out about data-driven marketing strategies. Marketers have discovered the wealth of knowledge and an incomparable advantage over the competition. With great power, however, comes great responsibility. Data-driven marketing is one of the most powerful tools in a modern marketers collection but along with such effective strategies comes a host of challenges that need to be met to ensure the greatest effectiveness and prevent unnecessary roadblocks that could easily be avoided.
A little foresight and the proper tools will make the difference between a great plan with limited execution and a paradigm-shifting marketing strategy that takes full advantage of personalized customer experience segmentation that increases loyalty time after time.
Asking the right questions
You don’t know what you don’t know. That’s why asking the important questions upfront is essential to an effective data-driven marketing plan. The first question (or one of the first) should be what stick are you using to measure success in the market? What are your key measurables? Identifying these essential growth points makes it easier to understand how Machine learning and AI can leverage the power of automation to assist in reaching those goals. Let’s work backward.
The key to a strong customer relationship is outlining a problem you target audience either has or is likely to have in the future and positioning yourself as the answer to that trouble point. How can you discover these problems, establish a need and identify a ready solution? These are the questions you need to answer and then ask yourself, using all of the data at your disposal, which of these are most likely to answer that question consistently? This is the best starting point. Mr. Rogers once said whenever there’s trouble, he looks for the helpers. Your job is to spot the trouble, using machine learning, large data sets and AI before anyone else. Then, ask yourself, ‘How can I help?’ this is a repeatable and powerful approach to creating optimized marketing campaigns.
Finding High Quality Data
Finding data is essential to big data analysis tools. It’s also easy to confuse information for quality insight. Not all data is created equally. Finding trusted sources for your demographics data can make the difference between marketing to a ghost population that has no discernible connection to the message you’re producing and targeting a specific subset that is highly engaged and tailored to your product. It’s important to vet your sources. Social media and similar solutions have high amounts of readily available data but reliability will fluctuate across platforms based on the strictness of the operating company, ease of fraud and difficulty involved with verification.
Whenever possible using secure, reliable data makes the difference in narrowing the scope of a campaign to optimal efficacy. Internal documents trump external forms and aggregates any day of the week. Identifying information pulled from government-issued sources will obviously carry weight over user inputted data and third-party resources should be trusted the least, even big-name providers where accuracy cannot be guaranteed. Building a marketing strategy around false information can even more damaging than doing nothing at all.
Defining a Normalization Strategy
It’s key to quickly and concretely define a strategy to handle data normalization before processes are in place that right requires a huge overhaul once the data flow begins and it becomes obvious that reporting doesn’t sync with anticipated models. Normalization involves understanding the subtle differences in actionable data sets and grouping them in a way that properly expresses these data points, taking into account that nor every customer is the same, not every job title is the same and sometimes similar or related items need to be organized to reflect their inconsistent natures.
For example, a sales manager might list their job title as Manager of Sales, Sale Mgr., Manager of Sales Services and on and on. Establishing a way to handle all of these similar and in some cases, identical reporting facets without double or underreporting entries requires diligence and a cohesive strategy. In markets where hand entry is common, it is even more important to put emphasis on consistency less vital statistics misrepresent on reports.
Analyzing and Interpreting Data
Once the right data has been gathered and normalized so that everything is available and reliable, it’s time to begin the analysis. Data sources, notoriously provide raw information than needs to be interpreted properly to provide valuable insights. Presented with big data sets, a marketer’s job is to pull relevant information and make crucial decisions based on the charting of the key data points. Data exploitation relies heavily on proper interpretation. This can be added largely by machine learning programs and the application of business-centric AI that layers understand how to how to take advantage of the many assets gathered together.
While a manual approach is technically possible and may even be advisable in rare, exceptionally detailed workloads, more often than not, analyzing data via well programed and vetted algorithms will prove to be the time and money saver many companies are looking for. This is the time to establish your goals, monitor heavily and be prepared to make adjustments at a moment’s notice. Once the results are in, test strategies based on this new information and most importantly don’t stop importing data now. This is the most important time to keep detailed records on how adopting the new strategy is affecting business over time.
Warning: While data-driven marketing does allow a company to turn on a dime and pursue an entirely new strategy at the drop of a hat, it’s essential that marketing plans have time to play out. Using the data provided the course of a campaign can make it easier to spot deficiencies, assess unexpected movements in engagement and determine strengths or weaknesses for future campaigns. Over time it becomes easier to coalesce data from previous campaigns to spot when it’s time to make a move and feel secure in that change, of course.
Connecting data with your team can be as easy as filling out a redirect link on a website, downloading and important a spreadsheet or as difficult as designing and running custom reports on back-end servers that may or may not be set up to provide that information on demand. Chances are your data sets will include a large collection of resources with varying degrees of difficulty.
Software providers have developed solutions that do the bulk of this work on their own, requiring less input than the manual method. However, a skilled marketing time will be able to converge and normalize data on a local or cloud-based server for ease of extraction and quick accessibility. However you choose to do so, the varying methods for interoperability and range of data types in possibly incompatible file types should be taken into consideration when choosing the best management software and processes to organize these resources.
Finding the Right Tools
You wouldn’t use a hammer on a screw and not every business needs the latest bells and whistles. Finding the right tools for your data is going to rely on several factors. First, the type of data you’re collecting is going to go a long way towards eliminating programs, processes and that will ultimately prove irrelevant. Just like data sets, it’s as important to eliminate unneeded tools as it is to toss useless information.
A bulky system designed to perform for the wrong type of business or wrong scale will create more problems than it will solve. Again, this is a time to work backward. Determine what your business needs from data-driven marketing and then search for providers that answer those specific questions. Now is not the time to reinvent the wheel as much as understanding what your business stands to gain from big data access and find the resources that are best suited to answer those needs.
When it comes to marketing, web marketing, digital campaigns and turning a scattered marketing process into an omnichannel data management system, data-driven marketing is a powerful tool that consistently achieves results when properly managed.
The job of big data is to analyze and optimize existing marketing strategies while providing the needed insights to create more powerful and effective campaigns in the future. In order to do this, attention must be paid to asking the proper questions, ensuring a quality team is in place and understanding how to read the data once it’s been collected and organized. Do all of these and a distinct advantage will begin to appear.