Data Technology Services to Support Marketing Analytics

Data Sources and Connectors

To begin, all of the data you need to collect from every source (e.g. paid search, website traffic and behavior, white paper downloads, email campaigns, direct mail, print, etc.) has to be brought together. For a marketer, this is all channels you use for marketing a product and all sources of information (ratings, social media, etc.) your customers might utilize. For advertising agencies, multiply this by the number of products and clients. This is a lot of data. For optimum marketing spend effectiveness, it needs to be continuously compiled and analyzed in real time.

Fortunately, there are a number of vendors that make data connectors available to a large number of standard sources of information (e.g., Google Analytics, Facebook, etc.). Unfortunately, they vary greatly in ease of configuration, reliability, price and ability to absorb data structure changes.

Loading the Data Warehouse – ETL vs. ELT

The next step is to create a place to house all this data. The industry jargon is data lake or data warehouse or data mart but really these are different approaches to a big database or set of databases. Each source has a different data structure, so this adds to the complexity. You also need to decide whether the first stop will be raw data or transformed data. Again, the industry jargon talks about whether you are going to extract the data, then transform it and then load it (ETL) or extract the data, load it and then transform it (ELT). At Apollo, we have a strong preference for ELT because it allows the marketer or agency to keep the connectors as stable as possible. In addition, changes in needs can be accommodated without any re-integration with the sending source. By change in needs, we are talking about deciding on a new type of analysis or another way of looking at the data. In ELT, you just change your transformation (create new views, combination variables, etc. ) which is completely under your control. Re-integrations are painful, time consuming and expensive because you have to get the attention of the source folks, agree on the changes, make the programming changes, test it collectively between teams, etc. It’s a big deal.

Customer Identity Management

For use in performing many advanced analytics methods such as multi-touch attribution, once you get the data in your database, you need a way to uniquely match all the data for each person across sources. In the jargon, this is identity management. At its essence, your objective is to create a master record for each person that contains all the known information about that person. When you get an interaction at a touch point, you then look at what you know from the interaction and try to map it to someone in your set of master records. You not only need to then tag that information with a unique internal identifier so you can use it in your analysis, you need to constantly evolve the data in the master record with each piece of new information. You need to be able to differentiate people at the same address and household. If using this data for personalization of communication, you need the context to be able to contact that person on a work email for work stuff and on a personal email for personal stuff. All the while, you need to be mindful of any restrictions on the use of Personally Identifiable Information (PII). This is hard. Again, you can program it yourself or get help from a few vendors.

Usable Data to Support Advanced Marketing Analytics

Finally, you need to set the data up for applying the complex statistical computations necessary. This may require queries across unlike data sets. In the industry jargon, this means structured and unstructured data (SQL and NoSQL) and text, images, etc. These queries can be across not only different structures/toolsets, but each database can be in different physical locations and vendors (think Amazon Web Services, Google, etc.). When you run the complex computations, all of the compute factors have to be scaled and capable of handling huge amounts of data so that the above factors can be computed across people and touchpoints. In a gross simplification, you can think about the data as a giant spreadsheet where the columns are individual touchpoints, and the rows are different people. Since people have different buyer journeys, many cells will not be filled in for all the possible touchpoints for each person (and journey for that matter). Then the statistical techniques underpinning advanced analyses such as multi-touch attribution are applied to the data in a column (as part of a complex multi-step process) to compute is weight or contribution to the buying process.

Artificial Intelligence (AI)

The usable data organized in the previous process can be fed into various artificial intelligence techniques to create models. These models can then be used to personalize the interaction of the marketer with the customer to provide only relevant information based on the customer’s interests and interactions and communicated over the customer’s preferred channels.

Artificial intelligence can also be used to automatically and in real time make adjustments to marketing campaigns, messaging, channel choices, etc.

For a company with a salesforce, artificial intelligence can be used to provide the right information to the customer at the right points along the buyer’s journey. In addition, it can inform the sales team when to reach out to the customers for an in person interaction.

Finally, artificial intelligence can be used to troll through the data and provide insights directly. In some forms, analysts can develop their own insights by using natural language processing (NLP) in a form similar to a Google search, but over your marketing analytics data.

Technology Guidance

One of the major complexities around creating the data technology needed for advanced marketing analytics, is deciding which combination of tools is matched to your company’s capabilities and needs, your budget, and your timeframe. It is also that many vendors want to get your data into their own proprietary environment so they can get the biggest piece of your budget. In theory, each vendor has optimized their components to best accomplish what they do, but this is not always true. Having your data in these proprietary environments can make integration with subsequent steps in the process complex and also result in duplicate data all over the place which makes control of the whole process difficult. So, while all vendors may claim they will smoothly integrate with other players in the marketing analytics technology space, in practice this often creates more complications.

Apollo’s Expertise

Apollo Consulting Group has deep expertise in information technology, complex database design and marketing analytics. Let us help you in selecting the right tools and in managing their implementation so that you can achieve your marketing analytics goals.