Data from digital sales and marketing sources continues to explode, giving marketers a potential bonanza of insights into their customers’ behavior.

But like gold prospectors and oil geologists, it’s not enough to know where the precious stuff is. In order to reap the riches, first they have to get it out of the ground and turn it into something they can use.

Sources including ecommerce sites, online video players, digital advertising, social media platforms, blogs, mobile devices, sales tracking systems and CRM tools are all generating huge volumes of data that hold both meaningful demographic and behavioral insights into customers. 

Recognizing its potential, a growing number of organizations are storing large volumes of this data. But the challenge they’re facing is how to turn the raw data collected from these channels into actionable insights. Consider Hadoop, the most prominent big data platform in use these days. Hadoop is open source software made popular from its use at Yahoo! and other big Internet companies attempting to glean useful customer information from large volumes of data.

Hadoop allows for cost-effective storage of very detailed user interaction data. However, in order to extract marketing value, organizations quickly need to get the information out of Hadoop and into a usable format: one that allows for simple, but powerful analysis leading to a better understanding of their customers, improved marketing and user experiences and ultimately greater sales.  But while Hadoop is an excellent platform for cost effectively storing lots of data, converting it into information of value is not that easy.

Compounding the situation, there’s a current shortage of developers with Hadoop experience, meaning many IT departments don’t have the expertise to extract the data into a usable format. To quickly leverage valuable information stored within databases like Hadoop, marketers need to be able to map their marketing goals to actionable key performance indicators. And they have to be able to do this rapidly, often in real time,  while the data still retains its value. It does little to no good to receive feedback on a marketing campaign six to eight months after it happened.

To accomplish this, marketers need to do two things: First, they must implement an agile marketing analytics process, and second, they need to establish a strong partnership with their internal IT teams, in order to build the appropriate marketing performance management infrastructure that can support their analytics process.

Ultimately marketers want to gain a real-time 360-degree view of their customers, track cross-channel marketing performance, and improve sales and campaign execution. But determining which KPIs are the most important to uncover the best sales opportunities can be an overwhelming task. This is especially true when they need to be tracked across two or more channels. Figure 1 (click here, or at left) depicts the overall challenge faced by marketers today.

The following are four steps for implementing an agile marketing analytics strategy that can help organizations analyze their data in order to achieve goals. Taken together, the steps describe an approach known as “agile marketing.”

Agile marketing was inspired by the agile software development movement that has revolutionized the slow and cumbersome software development process, greatly increasing the speed and realized business value of IT projects in the process. More recently, many of the concepts from the agile software world have been applied to marketing to increase the speed of results and embrace the rapid pace of digitally driven change.

Step 1: Assemble a cross-functional IT and marketing team

It takes a tight and effective collaboration between the IT and marketing to get past the hurdles to collecting, storing and extracting value from data generated from multichannel marketing.

The technologists need to understand the marketers’ business requirements, as well as the various formats in which the data is collected and stored, and the capabilities and limitations of the technical tools and infrastructure that support the marketing platform.

For their part, the marketers must communicate their marketing objectives and the KPIs associated with those objectives. They should also understand what data is being collected (or not being collected) as well as the types and limitations of the analysis to which it can be applied.  

To help bridge the knowledge gap between the marketers and the technologists, ideally, one or more members of this team should have backgrounds in both fields. An example would be an experienced technical business analyst with a strong marketing analytics background. 

Step 2: Identify marketing goals and KPIs

Marketing goals must be clearly defined. This should include specifics such as what customer interactions should be taking place within each channel and what metrics, or KPIs, will be used to gauge results.  For each KPI, it is important to ensure that the correct data is being collected and tracked in the right way. For instance, if you want to track the daily performance of a particular metric, IT will need to track and store the applicable level of data granularity each day. If the data needs to come from more than one marketing channel, IT will need to track and store the data across multiple channels For instance, if a marketing team wants to track which prospects are visiting the company’s website via its Facebook page, IT will need to be able to track the same users across both platforms.

Step 3: Map source data (e.g., Omniture, FreeWheel, Facebook Insights, Sales Systems, etc.) to KPIs

To determine if the correct data is already being captured to support the KPIs , conduct an audit. The audit should include all data sources, storage platforms and infrastructure, and should determine where the data is stored (SQL Server, Hadoop, etc.) and at what level of detail or granularity. For cross-channel KPIs, the audit should determine if common identifiers are in place to support a uniform set of analytics. Each feeder source needs to be evaluated to ensure that the proper level of information is stored in the SQL or Hadoop repositories. The audit should also identify any data that isn’t tracked but is needed to support all KPIs.

Step 4: Create an agile marketing implementation roadmap  

Rarely will the means of collecting all the data to support all the marketing goals and KPIs be in place. And even more rarely will it be possible (or affordable) to put all the missing pieces in place at the same time. In other words, the agile marketing team will have to prioritize.

A good approach is to develop a phased marketing analytics implementation strategy or roadmap for tracking the organization’s highest priority metrics first — those that will deliver the greatest business value. Given time and budgetary constraints, additional, less valuable metrics can be added  incrementally.

When the required data is already being captured, short development iterations to build out the KPI reporting capabilities should take place. Ideally, these should take about one to three weeks, even when extracting data out of Hadoop.

With the right resources on the team and the right analysis and presentation tools, data should be readily available for decisions and planning when it is needed. If the data is stored within Hadoop, extract snapshots of the data into a SQL database and OLAP cube for rapid analysis. Use familiar tools like Excel or SQL Server Reporting Services to manage and view the data.  

After each implementation phase is completed, the results need to be quickly digested and used to adjust the roadmap and priorities based on key learnings during that phase. This is a key part of the agile marketing approach. Don’t set the entire roadmap in stone and execute blindly. Learn throughout the project, and conduct course corrections along the way, using a feedback loop to ensure the marketing campaign is as effective as it can be.

With the right team of technical and marketing experts in place, a good set of target KPIs, and quick, focused interactions designed to glean key learnings and refine roadmaps, an organization can turn big data and agile marketing into a gold rush.

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