Some companies decide to ignore the topic in general, underestimating the importance
The expanding universe of big data brings in more opportunities for ventures to thrive, yet more risks inevitably follow, too. To escape the complexities, some companies decide to ignore the topic, in general, underestimating the importance of data science, big data analysis and business intelligence services for their marketing activities.
For good or bad, big data is not a fad that will disappear anytime soon. Over time, this trend will only peak, thus adopting it faster means getting an access to its advantages earlier. What advantages exactly? To answer this ‘what,’ we first need to understand why big data is important anyway.
Why big data matters
One of the reasons why some might underestimate the practical significance of big data is the hype and mystery around the agenda. Sometimes the wording is rather foggy, like “Big data helps you to transform raw information into a valuable resource”. For those who are hesitant about adopting any data science solution or creating their own, this sounds unconvincing.
If a company is able to sort out the data flows they constantly receive, they will acquire a pool of insights into their own practice. These facts should be used to compile dashboards and reports to bind different bits of statistics together and get the holistic view of the company. Then, this company can gain an advantage over competitors through a number of perks, such as:
- Enhancing a pricing strategy
- Using forecasting
- Adjusting customer segmentation
- Optimizing marketing campaigns in real time, and more
While companies across different domains can also enjoy other benefits of Big Data analysis, they can be too specific to fit all. Therefore, we'll review the general perks which can bring advantage to any industry.
4 reasons to use big data for marketing and sales
1. Accurate pricing
McKinsey states that a 1 percent price increase results in an 8.7 percent increase in operating profits. They also estimate that up to 30 percent of yearly pricing decisions actually fail to deliver the best price, which leads to an extensive amount of lost revenue.
As the art of pricing is critical to business success, nowadays it’s impossible to only rely on ‘trial and error’ or ‘gut feeling.’ Big data should be the centerpiece of decision-making, helping marketers identify what pricing strategies are working and which ones are flat-out.
To define a relevant pricing strategy, marketers need to gain insights on customer behavior across locations, product/service groups and seasons. They also want to consider the current goal, such as to attract new clients, boost impulse buys, increase occasion-driven or gift purchases and more. The resulting pricing strategies may be as follows:
- Differentiated pricing. It implies that a company offers different prices to different groups of customers, depending on why, how and when they make a purchase. A simple example is buying an airplane ticket, where the price changes according to the day of the week, time of the day, current plane occupancy and more.
- Versioning pricing. Big Data can give marketers a hint on how customers would like different versions of the company’s core product or service. Maybe, some clients can’t or won’t buy the whole package, but they’d purchase a stripped-down alternative at a lower cost. On the other hand, some clients would like to purchase a limited edition or a premium version.
2. Customer segmentation
With big data, marketers have access to each customer’s personal experience with the brand’s products and services. This information can come from every customer-related touchpoints, such as the corporate website, e-commerce platform, in-store software, social networks and mobile applications.
By bringing the data flows together, marketers then can segment customers according to the company’s current priorities, for example:
- Optimize customer engagement
- Increase customer loyalty
- Extend the post-sale pipeline
- Improve customer service, and more
Gaining the understanding of prevailing segments allows companies to evaluate their marketing activities in general. For example, if the segment of customers with low engagement (e.g. the ones with abandoned shopping carts) is expanding, it is recommended to review a sample of customers and their full journeys to define the critical touchpoints to fix.
3. Better forecasting
Predictive analytics implies using data, machine learning, and statistical algorithms to process historical data and identify the probability of certain future outcomes. It allows marketers to go beyond the events already happened and forecast customer behavior and sales. While the concept of predictive analytics itself has been around for years, big data enables marketing specialists to level up their efforts, such as:
- Increase the volume and range of information sources
- Advanced reporting
- Empower real-time forecasting
- Achieve more informed decision-making
- Employ a more detailed planning
Consequently, these efforts result in increased revenue. For example, the EverString and Forrester Consulting’s study on predictive marketing analytics have shown that predictive marketers are 2.9 times more likely to report revenue growth at rates higher than the industry average.
4. Tweaking campaigns in real time
The ability to send the right and personalized message at the right time is what determines marketing efficiency. However, as there’s no perfect marketing campaign (not from the first try, at least), flexibility is also a must.
Big data coupled with machine learning algorithms gives marketers the insights into their customers’ behavior, interests and engagement level. These insights are the practical opportunity to make campaign adjustments on the go, personalize messages and deliver them to customers in real time.
Examples are on the surface: the list of recommended movies and TV shows on Netflix; the “frequently bought together,” “more like this” and “customers also bought” sections of Amazon and more.
A company can show a particular customer only the products and promotions he or she wants to see, even including personalized offers and coupons to their phone when they walk into a store, coffee shop, beauty salon, etc.
So, is big data worth the hype?
We are ready to say a big bold yes. Of course, not every company needs to harness big data to the fullest. However, machine learning and data mining can come to the rescue when marketers across multiple domains ask such strategy-oriented questions as:
- What are customers’ top interests?
- What campaigns and discounts will increase sales?
- Will the new product become popular?
And hundreds of thousands of questions any company might have. When data analytics is applied properly, it’s like having your own corporate business consulting oracle. If you need to make fact-based decisions and if you need to stay at the top of your game, that’s the way to go.
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