This session is entitled “Increasing ROI with Data-Driven Decisions” and is moderated by William Grizack, Chief Strategy Officer at The Variable (@billgriz341).
Panelist include Jim Davidson, Manager of Marketing Research at Bronto (@JimSaidIt); Eric Holt, Account Director, Product Sales at Advertising.com (@aol); Mark Rockett, Founder & CEO at Rockett Interactive, Inc. (@markrockett); and Nadine Watson, Director of Southeastern U.S. Sales at Media6Degrees (@nadinesbest).
William Grizack opened up the session, asking the panel members to define “big data” and what important concepts are worth considering.
The general consensus revolved around the idea that the term “big data” applies to all the information collected across different metrics that describes your target audience, their personal profiles, their behaviors, and the way they connect with your brand.
Jim Davidson noted that it is important to consider this data across all marketing channels, as one may find the results vary widely.
Nadine Watson further commented that marketers tend to over-complicate the concept of big data, but that we can simplify this concept by concentrating on three major pillars: volume, velocity of that volume, and variety provided. Focus on using this information to drive the volume of your traffic, the reach and frequency of this traffic, and do it in a variety of ways that cater to each of the finer segments of your target audience.
In discussing how ROI can be easily defined, it was agreed that ROI represents the return generated from advertising costs. Panelists seemed to agree that it is important to segment your ROI goals based upon the target audience and/or the channel you are using (considering additional factors such as lifetime value received, the social sharing capacity, etc.). One can then use these ROI values to to influence their marketing decisions and choice strategies with regards to budget allocation, direction, messaging, and expansion opportunities.
Discussion topics shifted towards the use of pixel technology for the purposes of remarketing campaigns and other real-world applications. An example of this was provided as follows:
An online shopper adds an item to their shopping cart, where a this pixel is dropped in and captures that data. Should the shopper leave, the marketer can target this individual in the future with their cart information and/or similar products to encourage new, future transactions–and/or, potentially, sharing opportunities.
Additional applications for this data involve using this data to better understand your market. Jim added to this, highlighting that this information can drive innovation in products and services, along with creating a better brand experience for your user, connecting with them on a more in depth level.
Mark Rockett provided additional insight into how agencies can use this big data to appeal to clients. The most appealing quality, he described, is having a “holistic” approach with regards to gathering the information, understanding how to properly and efficiently analyze that data, and then implementing strategies that cater to optimizing accounts based on those results.
With the discussion turning towards the rise of Attribution modeling, Eric Holt emphasized the significant value behind tools offering this capability, as it allows marketers to better see the “story” behind consumers and how each marketing channel influences them. One may be surprised to see that certain channels (email, direct, organic, etc.) are more or less valuable than previously thought–and could, potentially, cause marketers to reevaluate their campaign focuses and/or move to reallocate advertising budgets in a more appropriate fashion. The secret to using these tools, however, is to focus on large windows of time to gain a clearer picture, which would, obviously, involve heavier sets of data.
Another important item often reiterated is that big data can often cause companies to reevaluate themselves based on what it is that their customers actually want. This could have a major impact on their company structure, their marketing efforts, and their corporate identity.
The question was then raised, “how does this internally impact agencies and the hiring process?”
Across the panel, all agreed that it is becoming increasingly important to hire those that can understand segmentation, the relationships between different metrics, and also can also analyze the data to see trends early on–all the while being able to contribute ideas that allow for capitalizing on those trends.
Nadine further noted that it’s predicted that 1.9 Million new jobs are expected by 2015 within the online analytics and data science areas, suggesting that both the technology behind big data is not going away anytime soon and that it will become increasingly relevant–if not absolutely necessary– to utilize these skills to improve the effectiveness and efficiency of online marketing.
So what makes for an ideal candidate? Panelist clearly agreed that a preferred candidate would be an individual that both recognizes “corporate vision” and has an analytical background, with the ability to apply that data in a way that aligns with a client’s ideal corporate identity and marketing goals.
What are some of the challenges towards using big data?
As many of the technologies and sources behind this data collection can present issues with regards to consumer privacy, it is important to have (1) a clear understanding as to how this data is gathered and used; (2) open communication between department teams, clients, and any legal teams; and (3) keeping up with the data in an efficient way that can be quickly implemented into advertising that applies to consumer behavior here and now–not after the trends are no longer relevant.
So where does one start?
Nadine Watson made a very good point: start by determining what your campaign goals are, and what metric values must be gathered to judge the success of those campaigns.
Mark Rockett focused on the next step: data collection, listing both paid and free tools (e.g. Google Analytics). This allows you to start retaining information. Begin leveraging that information along with data you may already possess (e.g. email lists, fans, etc.) to build these big data databases.
Eric added that sometimes smaller companies have to find ways to justify the utilization of big data. The ROIs received from strategies that involve big data will often speak for themselves. Should one question the value of big data (before being willing to invest in its use), it is important to remember that it is typically easier to promote transactions or user responses with individuals who are already familiar with your brand’s products/services, highlighting the advanced remarketing capabilities available with big data.
So what does all of this mean?
Simply put, it means that the best way to increase ROIs is to begin investing in the capability of learning more about your audience–and then using that information to optimize both your brand and marketing efforts.
For those of you who have a web analytics expert or department on staff, it also means recognizing their increasing value and showing them some appreciation, as the demand for an experienced expert will increase by 2015!
For those of you who don’t have a web analytics expert or department on staff, it means that staying ahead of the curve will soon require hiring an analyst and/or the consideration of engaging with an experienced agency that can provide these services.