Emerging big data use cases in digital marketing

A new Interactive Advertising Bureau (iab) study provides insights into the opportunities and challenges in leveraging big data for digital marketing.

Produced in conjunction with strategic consulting firm Winterberry Group and sponsored by IBM, the white paper reveals top investment priorities, high impact use cases and barriers to adoption around all things pertaining to big data in digital marketing. The findings are based on the results of an intensive research effort that surveyed more than 175 Digital Media industry experts spanning leadership roles in marketing, technology and analytics.

When asked about their top focal points for all future data-driven marketing activities, the high priority use cases zeroed in on four optimization problems in the Digital Media industry.

Big Data Use Cases in Marketing

1. Audience Optimization – It wasn’t surprising to find audience optimization at the top of the list as understanding audience behavior and marketing to them across channels with increased targeting precision directly impacts top line revenues. Even though most of the surveyed organizations considered audience optimization as a “foundational capability” that drives improved performance across branding, engagement and direct response functions in digital marketing; many believed that they were at the lower end of the maturity curve when it came to deploying this capability. Primarily because of the challenges involved in integrating large volumes of first and third party audience data, matching audience touch points across channels and running complex segmentation models to develop the right lookalike audience profiles.
The big data problem is around integrating large volumes of audience data across a wide variety of channels and data formats and processing them with accuracy and speed.

2. Channel Optimization – The rapid proliferation of digital media channels, the ever increasing need to tie together offline and online channels and respecting consumers messaging preferences are some of the key considerations why Channel Optimization was a top investment priority for digital marketers. According to one agency executive surveyed, integrating online and offline data for one of their advertising clients resulted in a nearly 30% increase in online display performance.
Digital marketers would like to understand the true impact of impressions across the entire purchase funnel, across different channels and touch points, to best direct their media buying efforts. Today there seems to be a bias towards channels that are last touched before conversion – such as search, or “sexy” channels – such as social, without necessarily understanding the impact of how other channels interact with audience in driving them towards conversion activities.

Performing multi channel attribution analysis and creating the right media mix model involves sophisticated correlation analytics over large volumes of digital event data.

3. Advertising Yield Optimization – Publishers realize that there is a huge opportunity in identifying and optimizing audience centric inventory. In order to improve advertising yield they are:
i. Sourcing ever-increasing volumes of data from exchanges and data management platforms to create richer audience profiles and overlaying that with their own first party data, forecasts and sales data
ii. Mining usage logs to uncover missed opportunities such as mispriced or ignored inventory
iii. Running sophisticated forecasting and analytics algorithms to maximize eCPMs

This combination of running fairly sophisticated math on large data volumes creates a big data challenge that needs to be overcome. Most of the publishers that participated in the survey rated themselves on the lower end of the maturity curve in their ability to solve this big data challenge and create a compelling in-house yield optimization solution.

4. Content optimization / Ad targeting – The emergence of automated trading desks and real time bidding platforms in digital advertising is enabling marketers to personalize their content (message) and target their audience in real time. For example, online marketers are able to establish dynamic targeting rules that customize offers based on a visitor’s on-site behavior. This approach enables them to shift efforts from top-of-funnel branding activities to those that lead to immediate conversions.
The need to identify, purchase and target high value customers across channels with the most optimized content, in real time, poses a big data challenge. The velocity with which targeting platforms need to act on streaming event data (audience behavior) and make content optimization and ad-targeting decisions is becoming an extremely critical aspect of digital marketing.

Apart from outlining the big data use cases and challenges the study also analyses what digital marketers would need to equip themselves with in order to capitalize on this opportunity.

You can download the complete research report here or you can click here to watch a reply of a DMA webinar with IBM Netezza and The Winterberry Group.

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