What is Data-Driven & Predictive Marketing?

What is data-driven marketing?

Creativity is an important asset in Marketing. But creativity doesn’t come out of thin air. It is fueled by the analysis of previous marketing efforts, by market and competition watch. And data is the key to successful marketing actions.

So, let’s set the ground for this article – what is the definition of data-driven marketing?

It’s the process by which marketers collect and analyze customer, prospect, market and competition data to take the best initiatives and optimize their chances to generate more revenue. Data helps you better understand your customers’ behavior and motivation to make a purchase.

With a marketing data-driven strategy, you can answer all kinds of questions to improve your performance such as: where are their prospects coming from? What’s the average time to convert? What drove them to become a customer? How loyal are they, meaning how often do they place orders? How do they interact with your brand and content? Who are your Brand Ambassadors? What is your most popular product and which ones make you lose money? Etc.

Understanding your customer

The better you know your customers, the more you can tailor your offerings and your services to meet their needs. The more you customize your communications to what your customers find relevant or entertaining, the more likely it is that they’ll even look at your campaigns.

Data about your customers is critical to help your brand compete, in fact, customers have such a high expectation of receiving super personalized messages that they’re likely to opt-out of your brand’s communications if they don’t get it. That’s why data-driven digital marketing strategies have such great power and potential.

So how far should marketers go? Let’s take a look at several options, from automation and retargeting strategies to some of the most sophisticated ways to leverage artificial intelligence and big data.

How to collect and analyze customer data?

Customer data is only relevant if it is explored, analyzed and interpreted effectively. Avoiding biases, efficient data visualization can help provide the right intelligence to your marketing strategies.

The volume of data that’s currently available to brands is astronomical, yet it is growing exponentially. Bernard Maar shares key statistics that will easily shake the confidence of anyone still doubting the potential of Big Data. Data gives brands tremendous power to fine-tune and customize their message. Internal data can give insights about customer consumption patterns; lifestyle data can help pinpoint ideal settings for future purchases. Company data can be used to monitor and respond to business performance.

How do you decide what type of data should be collected? Start with setting your objectives. First determine what you want to measure, project or discover, and only then choose what might help you input the right information for your analysis. Here are several examples of objective/data pairs:

Objective	Data
To improve the online customer experience	Google Analytics & social media
To improve the quality of the sales forecast	Market data and your own brand sales figures
To optimize your conversion rate	Advertising campaign data

A tailored message to every customer

By launching a marketing automation strategy, many companies have implemented data-driven marketing without actually calling it that. By inputting navigation data into a workflow, they can tailor each message to each step of the sales cycle. The message will be fully adapted to the context of the prospect, lead, or customer, making it that much more effective. The result is a better conversion rate and more “stickiness.” Let’s look at an example:

• Sarah signed up for a Pro Plan about three years ago. She has been very active on the Support Chat and has had over 150 sessions with your platform.
• Arthur signed up a year ago but does not remember doing it and has literally no clue who you are or what your product does.

If you haven’t been collecting all the pertinent data on them—time since sign up, number of sessions, number of conversations, etc.—you will have to send them both the same generic email. If on the other hand, you have been collecting data effectively, your marketing automation system will be able to send customized, personalized messages that bring the point home. Sarah could be rewarded for her engagement and loyalty, and Arthur could be reminded about how the product could help him if he let it.

Data-driven remarketing for e-commerce websites

Smart remarketing is also a good example of data-driven marketing strategies and is especially relevant to e-commerce businesses, small or large. By ensuring that data is collected at every step of the website visitor’s journey, e-commerce marketers can produce campaigns for every audience. Messages and communication channels can be adjusted to speak to the stage at which the visitor stopped their journey—and more effectively encourage them to take another step.

Did they end their visit after viewing just one product page? Did they fill their basket with a bunch of products, only to leave without purchasing? Did they complete a purchase but haven’t returned for more? Campaigns can be written that fit each scenario.

With artificial intelligence, more advanced strategies allow brands to predict the future of a customer’s journey. AI Marketing (AIM), sometimes called “predictive marketing” or “predictive analytics” anticipates marketing scenarios based on what has worked in the past. It can be used to gather data about past transactions and evaluate data from emails, meetings, and phone call content. It can relate the data to the outcome of the possible sales of your current and future campaigns and produce insights about patterns and make corresponding recommendations on smarter brand behaviors to increase sales.

Read also: Business intelligence in e-commerce: not just for large businesses

The power of predictive marketing

Predictive analytics is a clever mix of Big Data and Artificial Intelligence and includes a process to collect behavioral data about customers and prospects, including data about their purchasing journey. The AI then offers relevant optimizations and recommendations in real-time. The goal, of course, is to improve visitor engagement and optimize the conversion rate.

As discussed above, making the right offer to a customer is no longer enough for a brand to stay competitive. It must make the offer at the right time, on the right channel, and with the personalized message content. All of us internet users leave numerous digital traces behind us every day, whether they be on social networks, during browsing, or at the time of product purchase. Both behavioral and transactional allow brands to understand the consumer as a whole. Thanks to the data, we now know what the future customer is most likely to do according to a stimulus.

The power of predictive marketing also lies in its ability to adapt over time. Thanks to AI and machine learning, brands can continuously refine and deepen their understanding of their customers. With more understanding comes greater precision with which brands can reach their audience and evoke engagement.

For online retailers, it becomes critical to leverage the data in order to keep their customers on board for the longest possible time. Internet users today have a plethora of retail outlets to choose from, so with just a few clicks, they can move away from a brand to a competitor site. To avoid high churn, it’s essential for a brand to be able to detect the customers that are about to jump ship—whether it’s to unsubscribe or to visit the competition. But smart analytics can detect a trend: indications suggesting the customer’s dissatisfaction, such as comments on the web, or their disinterest in the brand, as indicated by inactivity in campaigns. With predictive analytics, a brand can identify those with weakening engagement and deploy loyalty campaigns before losing the customer altogether.

Sixty-two percent of internet users have suffered the discomfort of an online stock-out, often leading to the loss of that customer. That’s why it’s essential to optimize the supply chain by anticipating requests. Again, predictive marketing can allow a brand to refine its order forecasts by relying on the analysis of user purchasing and navigation data.

Marketers tend to trust their instincts, trying marketing approaches that are personal to them and whose performance they analyze only after the fact. While it’s statistically possible that this approach can lead to happy surprises, it can also waste time and money for mixed results. Trusting predictive marketing instead can save both time and money. Machine learning and artificial intelligence only make decisions based on precise mathematical analysis of a lot of data. This, in turn, empowers brands to invest resources in strategies with a guaranteed return on investment rather than to explore solutions whose results are difficult to predict.

Smart marketing automation

What else can a brand do that would elevate a business’ performance while making customers happier? Here are three examples of data-driven marketing activities that rely on smart content and automated processes:

• Chatbots. Global and digital businesses have to cope with being constantly available to customers on all possible channels. Social media channels are quite exposed, so the quality of services has to be impeccable. Chatbots can save the day by learning from the interactions and the most common queries and provide automated, smart customer service.
• Content Intelligence. Unlike content generation, this provides creators with data-driven feedback and insights for more effective content that will yield better results. With that input, brand new, tailor-made content can be produced upon each visitor’s profile.
• Dynamic pricing. A website’s bot can monitor each visitor’s experience including cookies, history, searches, and other activities, provide him or her with real-time pricing. This means fewer discounts and/or higher prices for the product or service that is most needed at the moment. The reverse applies to lower prices if the demand for a product is down.

In conclusion, if done right, data-driven marketing can be very powerful and lead to a better customer experience which in the long run will make your brand that much more appreciated. To accomplish that, you need capable technology to support your strategy, focusing on four key steps: capture, store, analyze and leverage.

ClicData helps aggregate all the necessary data in real-time and run predictive analytics scenarios by integrating with numerous AI-based software. Once the right data model is produced, the results need to be displayed in the right shape. Data visualization can serve as the bridge between data scientists and data-driven marketing professionals.