Digital Marketing Analytics: Driving Success with Data Insights

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Digital marketing analytics drive the success or failure of your business. Without the insights derived from a careful, appropriate, and accurate analysis of your data, your business is doomed to fail. Increasingly, that means using big data effectively as a tool for generating insights.

This post looks into the future of digital marketing analytics and how building insights from your data will continue to increase in importance.

1. Big data will increasingly stress marketing departments [Yana Trofimenko]
2. Structured data will lead the SEO charge [Mark Proctor]
3. Data integration will lead to sustained competitive advantage [Forbes]
4. Data delivers deeper customer insights [PACE]
5. Data from the entire customer journey build a brand [source]

Digital marketing analytics for the future

Big data will stress marketing departments

I can certainly vouch for this one. As a marketing professor for over 20 years, I can tell you marketing students don’t want to deal with numbers. Most picked marketing over accounting or finance because they wanted to avoid numbers. Sure, marketing is fun, but you need to be able to manage big data. And, the need to handle big data is just growing. I recently posted something based on a study by BCG (Boston Consulting Group) about the massive need for marketing folks with data dexterity.

Here’s what Yana had to say about marketing departments handling data analytics:

Decision-making in digital marketing is increasingly being led by data, so there will be more pressure on marketing departments to understand how to use this data effectively to generate real insight. The increasing competition on all markets will make marketers look for smarter ways to overcome their competitors and please their customers.

Not only should marketers master descriptive analytics to ground decision-making, but they also need predictive analytics to segment markets so they can develop custom strategies that optimize market performance in each market.

Pricing practices also have to change. Airlines have been doing this for years — using yield management resulting in a wide range of different prices based on localized demand and timing. This on-demand pricing is spreading to other industries. I just heard something today on NPR regarding pricing changing on a minute-by-minute basis, which is possible in a world where real-time analytics can drive digital offers.

Currently, experts estimate a deficit of over 2 million data analysts in the US over the next 4 years [if you’re looking for a job, analysts make six figures to start].

Structured data

Mark highlights the impact of using structured data to tell Google exactly what content is where on a page — reducing incorrect guessing by the Google Bot and bringing in more SEO juice to your pages. While structured data isn’t particularly new, JSON now handles the tricky task of adding Schema microdata without having to add code to every section of every page in your template.

Data integration

I ran into my pal, Shashi Bellamkonda at a holiday party the other day and he said he used to look at a large number of different data sources every day in his role as head of marketing for a large real estate developer. Now, he’s working for a DC-area startup using integrated data from many sources to fill your lead pipeline. No one has enough time or ability to do a good job of analyzing data that are spread all over the place.

Most challenging for integration is building a single vision of a consumer using both online and offline data. Merging digital with offline data provides keener insights to help with lead scoring and nurturing, according to Forbes.

Data delivers customer insights

Measuring sentiment was just the tip of the iceberg in terms of understanding customers. Sure, sentiment is better than nothing, but you need to dig a little deeper.

Personalization becomes possible with marketing analytics. We’re not talking about adding a person’s name on the webpage or email. Here, we’re talking about a deeper, more meaningful personalization resulting in predicting what content the visitors want using real-time analytics and apriori algorithms designed to deliver up just what the visitor wanted.

Building these algorithms is in its infancy with most websites relying heavily on a set of business rules to help deliver up the “right” content for a particular visitor. Designing algorithms and modifying those algorithms based on what the consumer already looked at is a big challenge requiring big data. Careful consideration of what data goes into the algorithm is crucial to developing a usable product as we know many types of data (especially social media) suffer by giving voice to only some segments of the total consumer market.

Digital marketing analytics for the customer journey

In marketing, we implicitly recognize that customers go on a decision journey when they buy products. This journey looks like this:

1. Need recognition: a gap exists between where I am and where I want to be
2. Information search: what products satisfy this need
3. Evaluation: which products are “best”
4. Purchase: got it
5. Post-purchase evaluation: happy, unhappy

In marketing, we’ve always called this the customer decision-making process, but the customer journey is a lot more fun.

Knowing where a customer is along this journey goes a long way towards building a process ending in the purchase of your brand and a post-purchase evaluation that’s good.

According to experts, digital marketing influences 71% of RETAIL transactions; representing about $2 trillion. Here’s what David Edelman of McKinsey says about managing the customer journey:

Yet tools and standards are changing faster than companies can react. Customers will soon be able to search for products by image, voice, and gesture; automatically participate in others’ transactions; and find new opportunities via devices that augment their reality (think Google Glass). How companies engage customers in these digital channels matters profoundly—not just because of the immediate opportunities to convert interest to sales but because two-thirds of the decisions customers make are informed by the quality of their experiences all along their journey, according to research by our colleagues.

Yet, most companies are much better at collecting massive amounts of data about the customer journey than analyzing it to bring actionable insights to guide direction. Most challenging is the vast amount of unstructured data (words, images, etc), which IBM estimates at 80% of all data produced in our world today.

With customers using a vast array of digital devices (from smartphones to tablets to virtual reality devices to IoT (Internet of Things) devices like Fitbit) and producing vast amounts of data on a continual basis, firms need better tools to automate data collection and build marketing analysis into the daily tasks of the firm. Because these devices are always on, producing data all the time, firms need to speed processes of marketing analytics to match customers to information.

For instance, a customer might begin a search based on an email opened on a smartphone then switch to a laptop or tablet for a more detailed search for information and they expect the e-tailer to remember them on each device to deliver an optimized experience. Then, when they visit the store, a beacon recognizes them and sends a discount offer to their smartphone or Google Glass. QR codes embedded on tags let customers do research in the store to discover more details about the physical products they’re viewing, thus merging physical and virtual customer journeys seamlessly.

What these digital marketing analytics mean for your firm

1. You need to move fast to develop a marketing strategy that incorporates sophisticated data analysis with tools that bring in a variety of data from online and off. If you don’t, your competitor will — and he’ll have you for lunch.
2. You need people who are comfortable with a certain level of uncertainty. Analytics are mostly probabilistic, rather than definite. You’re gonna be wrong a certain percentage of the time. But, playing it safe means you’ll win the battle and lose the war. Better to be wrong sometimes then provide a sub-optimal experience to everyone.
3. Upper management needs to understand the role of marketing analytics in driving overall strategy and clear roadblocks that challenge analytic initiatives such as poor communication and poor collaboration between functional departments and decentralize decision-making to allow business units to move quickly when a new trend comes to light.

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