Data: Accountability starts from the inside out
Are you using your data to make correct and timely decisions? Are you using all available data? Many quantifiable factors are currently available and can add layers of color to your internal data. For example, correlations can be made between internal sales and website & app behavior. This can then be correlated with weather conditions and events, power outages, competitors' import behavior, among other things.
Now more than ever, we have access to numerous data points via publicly accessible data sets like weather, traffic, Google searches, local mass media performance, among others. We can continually scrape the web for information like local used vehicle prices posted in online classified forums and websites. Also, through the Freedom of Information Act (FOIA), one can access troves of valuable industry information like competitors' average employee salaries and competitors’ shipment import records.
Accessing all this data is just part of the challenge; the real value comes from a correlation of these data points from the past and present to help predict the future.
Every organization has a wealth of data directly related to its clients. A common challenge is that this information is in different locations. A concerted data effort would unify this information into a centralized storage location that would help establish relationships between separate pieces of data. The whole is more than the sum of its parts, and by zooming into Key Performance Indicators (KPIs) at distinct points of the customer journey, organizations can help gauge their efforts to optimize their marketing plans close to real-time.
At Lopito, we are fans of The Digital Marketing and Measurement Model (DMMM) and use it as the base of our Business Intelligence toolsets. The DMMM is an analytics implementation & execution methodology inspired by the business-oriented analytics philosophy of Google's digital marketing evangelist, Avinash Kaushik.
We build the DMMM using an organization's digital data-generating properties such as websites, CRM platforms, social media outlets, paid media campaigns, organic search performance, among other sources. Once established, it's a flexible model that can be optimized to include even more data stemming from external sources, making the DMMM an ideal first step into the Decision Maker's very own and unique data journey.
In a nutshell, the DMMM is divided into three phases:
a) Assessment and Model Development
First, we identify the business objectives with the client's active participation. Then we identify goals for each business objective and define Key Performance Indicators (KPIs). From these KPIs, we establish the desired targets and identify relevant segments for each of them. This allows for multiple scales of analysis.
b) DMMM Implementation
In this next phase we set up the most appropriate set of tools for tracking, data storage, measurement, and reporting. We implement a proper tracking and tagging strategy and build templates for regular dashboards and ad hoc reports. It’s also important that we produce the appropriate documentation for future reference, adjustments, and evolution of the model.
c) Analytics and Reporting
Finally, we determine with the client the frequency of report updates and future revisions of the DMMM. Now begins the cycle of:
a. Regular reports
b. Regular presentations of findings, actionable insights, and recommendations
c. Evaluation and improvement of the DMMM
In our experience with LIH’s data efforts, we've helped clients harness the power of their already existing data by unifying data sources and creating analysis dashboards where they can self-serve their own inquiries. We love to help our clients thrive and overcome all challenges. Feel free to contact us so our data team can begin working on the development of your DMMM.