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The new normal:
data-driven marketing

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Being data-driven might no longer be a luxury, but a necessity for marketers. Seven in ten shoppers tested new digital shopping channels, and one in five customers switched brands. Within a few months, there was a ten-year increase in the penetration of digital technology in the retail industry.

However, because their organizations’ out-of-date data modeling was unable to capture these transformations with the requisite accuracy and speed, the ensuing spike of data may not have provided marketers with a materially better understanding of their clients. Many marketers have turned to mass marketing and promotions in place of trying to better target clients and customize messaging. However, some marketers are taking the data for what it is and pressing down on precision marketing rather than backing off. 
 
For instance, a consumer goods manufacturer expected that as towns came out of lockdown, sales of beauty items would soar. To prioritize where to spend their advertising budget, marketing teams followed reopening on a county level using epidemiological information, municipal reporting, and traffic data. Sales increased by double digits because of these strategies. 
 
A business service provider was able to seize on another developing trend thanks to similar findings. Small healthcare providers were developing far more quickly than other small and midsize enterprises in large metropolitan regions, according to data on business registration and employment. Armed with this knowledge, the company developed product bundles tailored specifically for the healthcare industry and launched paid media campaigns to target those establishments. Together with other, similarly data-driven initiatives, these actions have the potential to boost sales of a core product by more than 10%.  
 
Businesses that refine their precision marketing techniques in this way can significantly increase customer acquisition during times of turbulent change. To take advantage of this opportunity, though, brands will need to upgrade their modeling—from adding new types of data to retraining algorithms—to both keep up with evolving customer requirements and expectations and foresee changes in behavior. 

What is data-driven marketing? 

The strategy of perfecting brand messaging based on customer information is known as data-driven marketing. Data-driven marketers use consumer information to forecast their target market’s requirements, wants, and future behavior. Such knowledge aids in the creation of individualized marketing plans for the greatest return on investment (ROI). Nowadays, companies’ access to copious amounts of information can contribute to a data-driven media planning approach. Using applications or different websites to gather data, marketing teams can follow each brand interaction along the consumer journey with the help of good attribution modeling.  
 
Marketing teams can decide which creative assets generated the most engagements, which channels delivered the best return on investment, and other information after parsing and analyzing all this data. Organizations can fine-tune their campaigns based on these findings to guarantee the best customer experiences and the highest return on marketing investment.

Benefits of data-driven marketing

Consumers today are constantly exposed to brand messaging and marketing. They are now more selective about the messaging they choose to interact with as a result. Marketing teams can significantly raise the likelihood that members of their target audience will click on an advertisement, register for an online seminar, read a blog post, or take any action that contributes to a conversion aim by employing a data-driven strategy. 

Data-driven tactics enhance the customer experience and brand reputation because they help businesses understand the wants and needs of their customers. Additionally, they increase conversion rates since users are more inclined to respond to the highly focused content made possible by data-driven marketing. The top advantages of data-driven marketing include: 

Better Customer Experience

In-depth consumer profiles are used mostly in data-driven marketing to enhance the customer experience. This is necessary for success because half of the customers say they have left a website to buy a product somewhere else after having a terrible experience. 
 
Data-driven marketing’s enhanced personalization fosters pleasant customer experiences while building consumer-brand trust. According to McKinsey, personalizing the customer experience can increase ROI from marketing spending by 5-8 times. 

Better Attribution for Spend Optimization

Deciding where the advertising budget is being spent is a frequent problem for marketers. Marketing teams may decide which fraction of the advertising spend is having the biggest impact on conversions or brand awareness by using data-driven marketing supported by analytics. To do this, attribution models like unified marketing measurement (UMM) have been used to analyze customer journeys. To give a complete picture of the path to purchase, UMM considers multitouch attribution and media mix modeling. Businesses may find the factors that drive prospects and customers along the sales funnel, and then spend resources accordingly.

Relevent Prospect Decisions 

With two out of three marketers thinking that it is preferable to base decisions on facts rather than gut instincts, teams can make better decisions when they adopt a data-driven marketing approach. The use of data analysis enables marketers to base decisions less on theory and more on practical use cases. Data-driven marketing does not, however, ignore the emotional factors that may influence a consumer’s choice to make a purchase.  
 
To ensure that they are correctly balanced in campaigns, marketing teams may want to consider examining data using a framework that considers both rational and emotional decision-making. 
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Examples of data-driven marketing 

1. Data-Driven Website Chat

Not all visitors to your website are equally beneficial to your company. However, the same holds for website visitors who speak with you via live chat. Not every person who wants to speak with a member of your staff is the right client. Data can be used to help you decide who is utterly worth your attention. 
  
This is not to say that you should ignore everybody who talks to you online. However, you can use statistics to figure out how to react to website visitors. How you respond to someone depends on whether he or she is an established customer, a frequent website user (but not yet a customer), or a first-time visitor. Your team’s ability to enhance conversion rates through chat can be fueled by meeting customers and prospects where they are. 

2. Conversion Rate Optimization 

Data is fundamental to conversion rate optimization, but there is a catch: you need enough traffic to conduct reliable testing with software like Google Optimize. Alternatively, you can evaluate heat maps and individual user sessions in addition to gathering qualitative feedback using a platform like Hotjar. 

3. Effective Retargeting

Retargeting has been among the top uses of data-driven marketing. You should be able to produce a respectable return on investment through retargeting and remarketing if your product or service is valuable to the market and you are bringing in the correct kind of traffic. Since only people who have previously visited your website or a specific page will see your ads, it makes sense that the data being used here is that of your website visitors.
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Challenges to consider

1. The challenge of asking the right questions 

Asking proper questions may seem like an easy decision. It most likely is, too. How can you move your company forward if you do not know which questions to ask and which answers to seek out to address factual issues? Undoubtedly, knowing how many visits you received in a day or how long they spent on your page is beneficial. Will you, however, be any closer to achieving your key performance indicators (KPIs) if you do that? No. Consider your marketing aims as well as the questions you seek answers to. Do you want to increase the number of visitors who become paying clients? If so, you might want to concentrate on the channels that supply the most conversions. Remember: If you do not know what you are going to do with the data, it will not matter how good it is.

2. The challenge of finding high-quality data 

You might generate a huge amount of data every day when running a business. However, your data might not be sound enough to produce useful insights. High-quality data must, primarily, be reliable and current. One of the key traits of marketing data is timeliness, which enables companies to respond to customer needs as soon as they arise.  

3. The challenge of breaking down silos

The problem with marketing data is that it is not just large but also dispersed. It is disorganized, and it is not always simple to find exactly what you need. Your data is often dispersed and disorganized because it enters through many ways (just consider your social media presence) and is gathered by many teams (marketing, sales, finance?). As a result, you obtain several data sets, sometimes known as silos. Silos are a marketer’s worst nightmare since they do not give a single overview of all the output that is accessible and make it harder to spot data irregularities. The problem can be solved by dismantling the silos and combining all output in one location.

4. The challenge of normalizing your data 

It is time to concentrate on cleaning and harmonizing the data once you have access to all the pertinent data. Marketing data comes in a variety of formats because it is often gathered from many sources, and they must all be unified if you want to supply relevant insights from your study. Professionals might be forced to learn ever-more-specific procedures and approaches to organizing and evaluating their data because marketing is becoming increasingly specialized. Consider using a next-generation marketing analytics solution, which can complete the task for you in almost real-time. 

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Top data-driven marketing strategies 

1. Personalize the Customer Experience 

By customizing material and online interactions based on people’s demographics, buying histories, online behaviors, and other facts about them, businesses can best capture their attention. For instance, according to the marketing firm Adverity, DirecTV developed a customized marketing campaign that catered to those who had recently migrated.

 

When a person moved to a new place, DirecTV knew they were significantly more inclined to explore new services. The corporation created a customized version of its homepage that only those users would view by combining U.S. Postal Service data of change-of-address applications. Even though the conventional site offered new clients a $300 gift card, the customized page had a higher conversion rate. 

2. Coordinate Marketing Across Channels 

Identity resolution is a popular method for putting an omnichannel approach to data-driven marketing into practice. Identity resolution is “the ability to recognize an entity, be it a person, place, or thing, along with associated relationships, consistently and reliably based on both physical and digital qualities,” according to web marketing company Acxiom. The technique aims to coordinate marketing across channels based on the unique traits, interests, and technological footprint of each customer.

3. Use Predictive Analytics to Create an Ideal Customer Profile 

Businesses can target accounts that fit the ideal customer profile (ICP) by fusing predictive analytics with account-based marketing. An ICP enables a company’s sales and marketing teams to seamlessly coordinate their efforts to “steer the best prospects through the sales funnel,” according to digital marketing company Leadspace.

4. An AI-based solution for devising an ICP has three parts:

 
Predictive analytics can spot patterns of behavior. The patterns are converted into intelligence that distinguishes between high-quality (likely to result in sales) and low-quality clients (unlikely to lead to sales). 
 
An analytics engine and models can be driven by high-quality data. The data must be prompt, pertinent, correct, and readily available. The model’s accuracy and usefulness will be distorted by outdated and inaccurate data. 
 
It is necessary to transform team members’ knowledge into a format that the machine-learning model can import. Staff members must have immediate access to findings through the interface that links them to the analytics engine so that the model can be improved over time. 

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