Digital intelligence (DI) is transforming the landscape of digital marketing.

Savvy marketers and advertisers navigate the consumer journey through work in the three main pillars of DI:
Data Collection & Engineering
Using Tag Management platforms, we define dynamic hand-picked data that helps determine user intent. Through the use of dataLayers, JSON or browser storage, we make the data available for any 3rd party platforms, ensuring we tell a consistent story.
Data Extraction & Evangelism
Performing analysis on and drawing insights from incomplete data sets, getting so granular with information to track custom dimensions associated with individuals.
Data Science
Using human expertise and machine learning algorithms to examine the data from all channels and building campaigns from emerging trends.
This white paper will guide you through the basics of digital intelligence, from defining DI to success stories and the future of data analysis.

Digital Intelligence Defined

Good data in, good insights out
Digital intelligence is the ability to collect information, improve tracking, analyze trends, and find connections across all of your pieces of data. Here are the three main characteristics of DI:
Digital Intelligence Is Actionable Insight

The mission of DI is to grow brands by taking analytics beyond the confines of the statistician to inform the decisions of business leaders. To do this, DI experts musts be both marketers and data scientists, able to combine accountable, transparent data collection solutions with forward-thinking measurement strategies that translate metrics and KPIs into practical next steps.
Digital Intelligence Is Holistic

Digital intelligence cannot be successful when data and insights are held in separate channel silos. Traditional analytics may use paid insights to make adjustments in Google Analytics, but for DI experts, this is not nearly enough. Digital intelligence begins with a view of all channels, understanding how each one interacts with data and how people-based attribution models can be built at the intersection of data, channel, and insight.
Digital Intelligence Is Data-Driven.

Thinking “data-driven” isn’t just a philosophy. It’s a real attribution model, separate from standard position and rules-based models, that live and grow alongside the data. A data-driven attribution model understands that there are multiple permutations to each interaction. For instance, if a campaign uses a Facebook ad and an AdWords ad, how might the inclusion of a Bing ad help (or hurt) the effort? Data-driven models help marketers understand the relative weight and importance of each channel, and help to build a living, evolving model of the data that is more than just “data aware.”

Ready to see what a Digital Intelligence strategy can do for you?


What Digital Intelligence Can Do

Now that we know what digital intelligence is, let’s discuss what it can do.

Client Snapshot: T-Shirt Store

One of Wpromote’s clients, primarily a t-shirt seller, approached their team wanting to perform an in-depth analysis of their on-site search. The T-Shirt Store was looking to understand consumer bias post-search, meaning: why were consumers behaving the way that they did after they performed an on-site search?

They saw a dramatic increase in their on-site query volume for “shorts,” but the result was not an increase in conversion rates, but actually a decrease. When Wpromote’s data scientists looked into the issue, they found that the T-Shirt Store didn’t actually sell very many shorts. The limited product scope related to shorts could account for the greater number of searches and the deflated conversion rates.

By identifying the rise of this keyword before it solidified into a trend, Wpromote was able to recommend the T-Shirt Store look into producing shorts for the summer retail season.

Digital Intelligence Does More

Digital intelligence builds living models, identifies trends as they start, and provides actionable insights from huge reams of data. But DI can do more than that: in addition to connecting channels and platforms, DI can help build methodologies to connect online actions to offline conversions.

DI specialists understand which aspects of a campaign really drove growth
Consumer behavior after a search is often complicated and multimodal. Bringing the data related to their post‑search actions back in for analysis allows DI specialists to look at campaign performance down the line and understand which aspects of a campaign really drove growth, instead of just measuring vanity metrics.

Take, for instance, a keyword group which has five conversions and a second keyword group has one hundred conversions. It seems the first group is doing poorly, while on the second keyword group is more efficient, but it may be that those one hundred conversions are form fills, and are not leading to profitable action. DI understands that the quality of conversions is far more important than the volume of conversions. By understanding that, DI can look at the five conversion campaign and realize those five conversions are actual sales, and serve as a much better touch point than the one hundred form fills.

Performing Predictive Analysis

Customer lifetime value (LTV) and profit modeling are deeply important from an audience segmentation perspective. When engaging with consumers online, DI specialists want to have clearly defined audiences to understand who they should be engaging with based on segmentation strategy -- and how much more should be paid for certain profitable segments. Analysis through LTV lets marketers know that the smart campaign decision may be to bid on an expensive $5 keyword because doing so will bring in the profitable long-term customers.

For more information on the power of LTV and profit-modeling, check out our whitepaper.

LTV isn’t the only focus of digital intelligence, however. The advanced capture of data on websites represents a huge piece of the DI puzzle. The very structure of a website can determine how data passes between pages and goal completions, and purposely building a site’s architecture to smoothly capture unified data can have a huge impact on subsequent business decisions. At a development level, thinking about data can inform site design and lead to profitable business outcomes.

The Future Of Digital Intelligence

The future of digital intelligence is bright and busy, and there are a number of trends savvy marketers should keep an eye out for:

Data Privacy

As data privacy grows in importance and necessity, digital intelligence specialists must make sure that we are not only compliant now, but also proactively compliant in the future. To do this, marketers and businesses must ensure that any DI methodology used is future-proof, guaranteeing opt-out options for the wary consumer. While DI will continue to focus on the collection of granular data, that data must be voluntarily given and always collected in the interest of the consumer.
Data Warehousing

Bringing data into one “giant container,” or one big database, will allow DI specialists to better identify trends across the board. Such “data warehouses” will also help to build better predictive models for both profit-driven marketing and predictive analysis. While predicting LTV will remain critical to digital intelligence, what businesses and brands really look for is the ability to predict trends before we see them. Data warehousing allows marketers to build into the next trend.
Machine Learning & Predictive AI

Machine learning and predictive AI are the future of DI and smart digital marketing for forward-thinking advertisers and brands. AI shows amazing promise in sifting through enormous data warehouses to detect trends before the human eye can see them, allowing your business to be the first to market.

Machines and machine learning will be the future of finding anomalies and better understanding how we can forecast upcoming shifts in the market. Machine learning and predictive AI will more accurately predict Ecommerce trends and what they’ll look like, using that information to drive investment and change how businesses build their budgets.
Incremental Experiments

Carefully planned incremental experiments will be another focus of DI as specialists and marketers seek to discover the net impact of different initiatives, like online to offline. Iterated incremental experiments will continually develop and fine-tune DI’s living models, helping marketers discover the answer to questions such as: how can we drive people to stores while being privacy compliant? In which critical areas are we missing opportunity or over-investing?

Wpromote Does Digital Intelligence Right

Wpromote’s digital intelligence services are a cut above the rest. Our team of data scientists have the specific skills and experience to enable your business leaders to leverage the benefits of DI.

Are you ready to join the digital intelligence revolution?

 
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