Top 3 Reasons wy Mrters Struggle with Data. Learn what they are from Blueshift and learn how to overcome them.

Why Do Marketers Struggle with Data?

Our recent study shows that activating data is crucial to successful artificial intelligence (AI). However, 92% of the companies we surveyed said that they struggle with one or more of either data access, unification or analysis that prevents them from making better use of their customer data (see figure 1).

Old news to you? Perhaps, but the ever-increasing importance of data in marketing will ensure that those who cannot harness their data are going to be left behind. The problem becomes clearer when we drill down into each of these areas to better understand the situation and look for potential solutions.

Figure 1: Top challenges faced by  marketers

Data Access

Data access is the first and most fundamental step to working with data. It gives marketers the ability to use data from multiple sources in near real-time. Today’s marketers need quick access to data stored or generated from multiple systems both inside and outside their organizations. Here are some examples:

  • Customer record data from the CRM system
  • Purchase data from e-commerce and traditional point of sale systems
  • Campaign response history from the marketing system
  • Customer service data from the support system
  • Visitor and browsing data from the website
  • Ad clicks from advertising networks and social media networks
  • Social media activity and interactions from those systems

With so many sources of data, it’s not surprising that 46% of all marketers consider data access one of their top challenges. Besides the numerous sources of data, two other factors impede data access:

  1. Systems that are monolithic and focused on specific tasks but not designed for easy data access. Legacy systems, in particular, were designed to work as fully contained units where information extraction was episodic and infrequent rather than continuous.
  2. Organizational processes that emphasize guarding the data and limiting access to it rather than sharing it. Usually IT departments serve as guardians and gatekeepers to data access.  In our study, marketers who were given advanced access to data were 1.6x more likely to be using a majority of their customer data over those who had to go through IT to get access to this data.

All of this results in balkanized data that is hard to get to and difficult to access in real-time.  Solutions such as data warehouses and data lakes were designed for a “store and analyze” approach which is ineffective in today’s real-time environment.

Data Unification

Data unification refers to the ability to bring together data from multiple sources and connect them together. A unified view of customer data is a good example of this because it enables marketers to better engage with customers by offering them personalized experiences on not only their profiles (gender, preferences, purchases, etc.) but also their behavior (website visits, catalog views, mobile phone activity, current geographical location and social media activity).

McKinsey & Company refer to this as a 3D-360° customer view (see Figure 2) that comprises data from many sources that get updated often and needs to be used in almost real-time to see its value.  41% of marketers in our study considered this to be one of their top challenges.

Figure 2: 3D-360° View of Customer

Data Analysis

Analysis is perhaps the hardest of these functions so it’s probably no surprise that 54% of all respondents in our study considered this to be a top challenge.  As a 20-year data-driven marketer, I attribute the data analysis challenge to three things: lack of a strategic approach to the analysis, missing data analysis skillset in many marketing teams, and deployment of the wrong technology.

It all starts with strategy.  Very often marketing teams jump into data analysis expecting to dive into the data in the hopes of an answer revealing itself.  And very often, such efforts take a lot of effort and don’t produce the results expected.  It’s the “dumpster diving” of data analysis. Instead, it is far more effective to start with one or more hypotheses, frame the questions you want to answer and then start the analysis.

There’s no substitute for having the right people. Data-driven marketing has been the domain of a relatively small number of people and good talent is hard to find.  This leads to marketing leaders confusing people who know how to create reports and run operations with those who can effectively frame the problems they are trying to solve and use analytical techniques to solve those problems

Use the appropriate technology to solve the problem rather than using the latest technology hoping to get the answers.  This starts with setting up the problem correctly, identifying use cases and then using the right technology that collects and helps analyze the data.

McKinsey & Company go beyond these three general areas to identify several other more specific issues in their recent report, Ten Red Flags Signaling Your Analytics Program Will Fail.

The Way Forward

Data has been called the “fuel of the new economy.” And just like during the oil rush of the late 19th century, everyone’s drilling. Smart organizations will take a thoughtful and fresh approach to getting the most out of their customer data by:

  • Opening up access to the data so marketers can utilize most of their customer data
  • Providing a unified view of the data by dynamically accessing the data from multiple systems rather than trying to consolidate everything in a single system
  • Analyzing the data by first developing a strategy and process for developing hypotheses and testing each hypothesis by asking the right questions.

Learn how to overcome your customer data struggles in this new independent research, “Activating Customer Data for AI-Powered Marketing”. Get your copy now.

Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

5 Predictions on How AI Will Reshape Marketing

5 Predictions on How AI Will Reshape Marketing

According to the Tata Consultancy research cited in the Harvard Business Review, companies implement AI primarily to increase the effectiveness of information technology, marketing, financial trading, and customer service. Though AI promises many things in the field of Marketing, today’s deployments are far more pedestrian and focused on anticipating customer purchases or improving the efficiency of various marketing activities like monitoring social media.

However, AI’s impact in marketing over the coming years will be wide-ranging and far more significant than it is today. Here are the 5 key changes that we see taking place.

#1 Customer experience becomes more personal, less robotic

AI will make the Customer Experience more personal, and paradoxically less robotic.

This may sound counterintuitive, but customers are quite comfortable with bots. Research by Pega Systems shows that 55% of all customers surveyed were comfortable with a business using AI to interact with them. 19% were uncomfortable and 26% were neutral. Many respondents felt that bots can help improve customer service particularly in retail, healthcare, and telecommunications. Anyone who has had to navigate through a phone tree when calling their local phone provider will empathize with this sentiment.

However, customers would rather not encounter bots that cannot give them a contextual experience. Aftersales support, for example, is an area where customers would rather deal with a human being. They want real conversations.

Recent innovations in AI are aimed at addressing such gaps. The rapid innovation in technologies like Siri and Alexa are harbingers of things to come.

#2 From driver-assist to self-driving marketing campaigns

Just as AI technology in cars is moving from driver-assist to self-driving cars, AI in marketing will evolve from improving tasks like segmentation to helping orchestrate an entire campaign.

Most AI technologies for Marketing are designed to enable marketers to streamline manual and repetitive tasks. In the future, this will change. We will see AI playing an important role in orchestrating entire campaigns. The campaign builder technologies of today will be replaced by AI-powered campaign builders that will use data to personalize messages and offers, choose the best channel mix and the best times to execute those campaigns.

Forward-thinking marketers are already taking advantage of AI to connect customer experience from the top of the funnel down to the bottom. In our recent survey, for example, marketers revealed that they are using AI to overcome their top challenges in orchestrating customer experience at the top of the funnel. More than 40% of surveyed marketers are using AI for audience expansion, while 39% are using it for audience targeting.

#3 More time on strategy, less on manual operations

AI will give marketers more time to spend on strategy and less on manual operations.

Collaborative filtering and offer optimization are just some of the many aspects of marketing that AI can optimize. Any problem that involves the analysis of large amounts of data to see patterns, AI is a good tool to tackle that problem.

This leaves marketers with the not only the time but also convenient access to results from the analysis of large amounts of data. Marketers should interpret these results and factor them into their future strategy.

#4 More creative story-telling with expanded reach

Marketers will once again become creative storytellers, with AI helping them scale their storytelling to millions of users.

According to MarTech Advisor, 95% of content has no impact on engagement and is largely wasted. Marketers should combine the “old school” skill of storytelling with new AI-powered techniques like sentiment analysis to both create and deploy the most engaging content that people will consume.

AI can also be applied to distribute this content in a highly personalized manner based on the preferences and content consumption behavior of the recipient.

#5 Thrive or die based on the use of AI

Brands will either thrive or die based on how well they adopt and use AI in marketing.

Recent research by Deloitte shows that AI has sparked a renaissance in modern retail. An industry that was not so long ago in a “retail apocalypse” has bounced back and grown faster than overall GDP every year since 2009. Brand leaders have invested in AI to better personalize the retail experience for their customers.

Most industries will face a similar challenge and will need to figure out how to adopt AI or be at a competitive disadvantage. Data access and activation will be a key determinant of success. Our research shows that businesses that use more than 75% of their customer data in their marketing and 1.4 times more likely to exceed revenue goals than those who don’t.

Interested in learning more? Download the full report here.

Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

From Acquisition to Retention: Top AI Techniques for Today’s Marketing Leaders

Top AI Techniques for Today’s Marketing Leaders (From Acquisition to Retention)

In our recent survey, more than 60% of marketers revealed that they are planning to increase their usage of AI going forward — proof that more and more marketers are realizing AI’s potential to enable greater marketing success.

However, very few marketers are taking advantage of advanced AI capabilities that can not only enable them to do more than just acquire new prospects but also convert them into loyal and more valuable customers.

Incorporating AI into All Aspects of Marketing

How marketers integrate AI into their strategies can determine their overall marketing success. They should consider AI as an integral component of their entire marketing game plan instead of treating it as a mere tool to bolt on when needed. Even before they consider specific AI techniques to implement, they should first look at their strategy for using AI by asking the following questions:

  • How can AI help us better find and engage new prospects?
  • How can AI enable us to engage customers?
  • How can AI turn our existing customers into more valuable customers?

Here are our recommendations on using AI for these three key aspects of marketing.

1. New Prospect Acquisition

Acquiring new prospects can be a very difficult endeavor no matter the size of the business. Increasing [brand] visibility and generating quality leads are two of the biggest marketing challenges businesses face.

Traditionally, marketers employ highly manual, time-consuming, and blunt approaches to win new customers. To get a decent list of prospects, for example, they use demographic data such as industry and job title to purchase lists, get referrals, and harvest their website visitors. After pulling this data into their systems, they then have to start engaging with these prospects to further narrow down this list to pinpoint and target the right customers.

AI can give marketers the capability to obtain relevant and useful information quickly using behavioral data. In fact, almost half of surveyed marketers (43%) use AI primarily to acquire new prospects using audience expansion techniques such as the following:

Look-alike audience expansion.
AI can be applied to the demographics, preferences and behavior of your existing customers to develop predictive scores of your best customers. You can then use this set as a seed list on a large network like Facebook to acquire similar audiences there.

Targeting and re-targeting users.
Almost 40% of surveyed marketers use AI techniques to better target audiences on large networks such as Facebook and Google. AI can be applied to prospect behavior to develop predictive scores that can then be used to re-target prospects with specific offers on the large networks. Similar techniques can be used to re-activate existing customers and prevent them from churning.

Percentage of marketers using AI in their marketing today and how they are using it

Tip: While these AI-powered techniques are helpful, marketers should put the same or even more emphasis on the other stages of the Buyer Journey. Which brings us to the next point.

2. Delivering Personalized Customer Engagement

One of the biggest challenges for marketers today is around delivering personal and content-rich experiences at every touchpoint in the customer journey. AI has a lot to offer today to enable marketers to intelligently and insightfully engage their customers on a highly targeted basis, with recommendations and micro-segmentation.

Marketing Stats - Percentage of Marketers using advanced AI in thier marketing

Using AI-enabled technologies like collaborative filtering, for example, marketers can predict customers’ interests based on the preferences and behaviors of others similar to them. But despite the proven capabilities of collaborative filtering, only 6% of marketers are using it. Similarly, only 16% of marketers are using predictive techniques to learn user preferences or affinities for various products and services based on their behavior and to segment them using these affinities.

Tip: To win today’s customers, marketers should provide them with highly personalized content. To be able to do so, they should apply advanced AI technologies like collaborative filtering and predictive affinities to the engagement phase of the Buyer Journey.

3. Activating Your Customer Data to Retain More Customers

According to Harvard Business Review, “acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one.” So if marketers want to save on costs while keeping their sales up, they should focus on creating more value out of their existing customers. To do so, they should harness both historical and real-time customer data—but herein lies the problem.

The majority of marketers (54%) are using less than half of the customer data they have. As a result, they are not taking advantage of the potential in this data to help them improve customer engagement and increase share-of-wallet. The study results also show that those marketers who have been able to get advanced access to their data are 2.4 to 2.8 times more likely to deployed AI techniques like predictive affinities and collaborative filtering.

Marketing Stats - Markters with greater access to customer data are up to 3xs more likely to use advanced AI in their marketing

Tip: To make the most of their AI investments and deploy leading-edge AI use cases, marketers (particularly non-technical marketers) should have advanced access to data and should activate more customer data.

Data Fuels AI-Powered Marketing

Marketers will only unlock the potential of AI if they can first get advanced access to their data and then apply AI techniques to improve engagement all through their buyers’ journey.
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