Big Data Case Study Marketing

Are you looking for some of good case studies that highlight how large companies leverage Big Data for driving productivity? Check out these 17 important case studies on Big Data.

23andMe

23andMe is a privately held personal genomics and biotechnology company. The company has developed its whole model around pulling insights from big data to give customers a 360-degree understanding of their genetic history.

CBA

Commonwealth Bank of Australia is using big data to analyse customer risk. Using analytics can get better risk assessment businesses, ongoing cash flow performance and early warning of risk challenges.

Centers for Disease Control

The Centers for Disease Control and Prevention (CDC) is the national public health institute of the United States. Its main aim is to protect people health and safety through the control and prevention of diseases. CDC had to rely on doctor reports of influenza outbreaks. CDC was weeks behind in providing vaccines to the affected patients. Using historical data from the CDC, Google compared search term queries against geographical areas that were known to have had flu outbreaks. Google then found forty five terms correlated with the outbreak of flu. With this data, CDC can act immediately.

Delta

Delta Air Lines, Inc. is a major American airline with an extensive domestic and international network. In general the top concern for an airlines would be passenger’s lost baggage. Delta looked further into their data and created a solution that would remove the uncertainty of where a passenger’s bag might be.

Energy Future Holdings Corporation  

Energy Future Holdings Corporation is an electric utility company. The majority of the company’s power generation is through coal- and nuclear-power plants. The company used Big data to install smart meters. The smart meters allows the provider to read the meter once every 15 minutes rather than one month.

Google

Google constantly develops new products and services that have big data algorithms. Google uses big data to refine its core search and ad-serving algorithms. Google describes that the self-driving car as a big data application.

Kreditech

Kreditech is a young tech company headquartered in Hafencity, Hamburg. The European company uses Big Data to create a unique credit score for consumers using more than 8000 sources. The analysis also lead to a surprise discovery of correlation between social media behaviour and financial stability.

LinkedIn

LinkedIn is a business-oriented social networking service. Founded in December 2002 and launched in 2003, it is mainly used for professional networking. LinkedIn uses big data to develop product offerings such as people you may know, jobs you may be interested in, who has viewed my profile and more.

McLaren’s Formula One racing team

McLaren Racing Limited is a British Formula One team. The racing car team uses real-time car sensor data during car races, identifies issues with its racing cars using predictive analytics and takes corrective actions pro-actively before it’s too late.

Mint.com

Mint.com is a free web-based personal financial management service for the US and Canada. Mint.com uses big data to provide users information about their spending by category and have a look where they spent their money in a given week, month or year.

Singapore healthcare

The healthcare providers in Singapore used analytics to better understand each patient’s condition, lifestyle choices, work and home environment. They can create personalized treatment plans tailored to that person’s individual behaviour.

Sprint

Sprint Corporation, is a United States telecommunications holding company that provides wireless services and is also a major global Internet carrier. It is the third largest U.S. wireless network operator as of 2014.Wireless carrier Sprint, uses smarter computing – primarily big data analytics to put real-time intelligence and control back into the network driving a 90% increase in capacity.

T-Mobile USA

The mobile operator has integrated Big Data across multiple IT systems to combine customer transaction and interactions data in order to better predict customer defections. By leveraging social media data (Big Data) along with transaction data from CRM and Billing systems, T-Mobile USA has been able to “cut customer defections in half in a single quarter”.

UPS

United Parcel Service of North America, Inc., referred as UPS , is one of the largest shipment and logistics companies in the world. The company tracks data on 16.3 million packages per day for 8.8 million customers, with an average of 39.5 million tracking requests from customers per day. The company stores over 16 petabytes of data.

US Xpress

US Xpress, provider of a wide variety of transportation solutions collects about a thousand data elements ranging from fuel usage to tire condition to truck engine operations to GPS information, and uses this data for optimal fleet management and to drive productivity saving millions of dollars in operating costs.

Verizon

Verizon uses big data to enhance mobile advertising. A unique identifier is created when the user registers in the website. The identifier allows advertiser to use information from the desktop computer. Marketing messages can be delivered to you mobile phone using this information.

Woolworths

Woolworths is the largest supermarket/grocery store chain in Australia. Woolworth uses business analytics to analyse customers’ shopping habit. The company nearly spent $20 million dollars to buy stakes in data Analytics Company. Nearly 1 billion is being spent on analysing consumer spending habits, and boosting online sales.

Predictive analytics plays a critical role in every step of your customer’s lifecycle. This post walks you through eight different use cases for predictive analytics in marketing.

What is Meant by Predictive Analytics?

Predictive analytics involves any activities that leverage existing customer data to make intelligent assumptions about the activity of future customers.

A successful predictive analytics strategy hinges on a few key criteria:

  1. Clean, Quality Data to Decipher

A recent post on the ReachForce blog shares specifically why predictive analytics and clean data go hand-in-hand, and is worth a read.

In short, because any predictive analytics strategy is founded on your existing customer data, a database of clean, quality customer information remains the single most critical component of a successful predictive analytics strategy.

Without robust customer data, any attempt at leveraging predictive analytics might actually prove detrimental to your business by alienating customers and missing opportunities through incorrect or invalidated assumptions.

  1. An Experienced Marketer or Data Analyst

Recognizing trends in your data requires extensive training and experience, which is why an experienced marketer or trained data analyst on staff becomes a “must-have” once your marketing team graduates from predictive analytics basics to some of the more complex use cases included in this post.

  1. The Right Tools and Solutions to Collate Data and Implement Your Strategy 

From a data management solution that collects and unifies data across all marketing channels to the right automation tools to carry out the strategy you develop based on predictive insights, having the right tools in place should be a big focus for the marketing technologist on your team.

Once you have these three components in place, how you use predictive analytics continues to expand into new, exciting opportunities for marketers.

Here, then, are eight predictive analytics use cases proven to have a measurable impact on marketing ROI.

Why Predictive Analytics is Important

Predictive analytics starts influencing your strategy long before a prospect even converts to a lead in your funnel.

That is the cyclical nature of predictive analytics; as leads convert to customers, the data gathered from those new customers influences the next generation of marketing activities.

Here are two ways predictive analytics impact your top-of-the-funnel activities:

#1. Improved Lead Scoring

Ask any salesperson and he or she will tell you:

No two leads are created equal. 

While that may be true for prioritization purposes, predictive analytics teaches you how to correlate the actions of your existing customers to influence your future efforts as a marketer.

That becomes most apparent when looking at the insights a good data analyst can glean from basic demographic and behavioral customer data, particularly in regards to lead scoring.

Historically, lead scoring has been a collaborative task between sales and marketing in which salespeople tell marketers, “these are the leads I want passed to me right away,” and then marketing creates a “score card” of sorts that measures the potential value of an inbound lead and determines (hopefully automatically, though some teams stuck in the stone age may still be managing this as a manual process) whether the lead is “sales-ready” or needs to enter a nurture campaign.

With the power of predictive analytics, lead scoring becomes less of an anecdotal list of criteria from sales and more of an actual data-driven view of your target customer.

When combined with a good automation tool, rules governed by predictive analytics can quickly score leads based on demographic, behavioral, and psychological data. Those scores determine whether leads are “hot” and should be immediately contacted by sales, or if they need more time in a nurture campaign before moving further down the funnel.

#2. Refined Segmentation for Nurture Campaigns

For those leads still in the early stages of the buying process, defining an appropriate plan for lead nurturing should be the natural next step.

Lead nurturing does not take a one-size-fits-all approach.

Instead, the best campaigns for moving leads toward becoming “sales-ready” use segmentation to create customized nurture tracks. Demographic and behavioral data tells you the right level and type of content to help push leads further down the sales funnel. Predictive analytics is, of course, the mechanism that makes that possible.

Predictive Analytics as a Means of Customer Segmentation

In addition to appropriately aligning leads to the right nurture campaigns, predictive analytics helps marketers segment customers in a number of key ways, including:

#3. Improved Content Distribution

Your team likely invests a good portion of your marketing budget in quality content.

This makes sense because content marketing has the ability to provide significant ROI for your company. There is nothing more frustrating than putting a bunch of money into developing content, only to find no one opens or reads it.

Often, the content itself gets the blame here when, in reality, the true culprit is an ill-defined strategy for content distribution. Predictive analytics tackles that problem head-on by analyzing the types of content that most resonate with customers of certain demographic or behavioral backgrounds, and then automatically distributing similar content to leads that mirror the same demographic or behavioral habits.

#4. Predictive Lifetime Value

You likely know that the true measure of marketing ROI is your customer’s lifetime value.

Did you know, though, that number can actually be predicted based on the same predictive analytics strategies that help you more accurately distribute content or score leads?

When you look at the historical lifetime value of current customers that match the backgrounds of new customers, you can very simply make a reasonable estimate of that new customer’s lifetime value.

#5. Propensity to Churn

Similarly, protecting your baseline becomes much easier as well when you start leveraging the power of predictive analytics. How?

By learning from past mistakes, of course. Past behavior is indicative of future behavior and nowhere is that more true than with your customers. By analyzing the behavioral patterns of previously-churned customers on your platform, a savvy marketer can identify the warning signs from current customers and either notify the sales partner responsible for managing the customer relationship, or automatically plug the candidate into a churn-prevention nurture campaign.

#6. Upsell and Cross-sell Opportunities

Finally, marketers can leverage that same customer data to also identify upsell and cross-sell opportunities. Much like the ability to forecast customer lifetime value, one can use data from customers who have added on after the initial sale to predict future customer growth.

Predictive Analytics and Data Visualization

As mentioned earlier in the post, predictive analytics is cyclical in nature; new insights feed future marketing decisions, which yield new insights that then feed the next set of decisions, and so on.

That loop back to top-of-the-funnel activities occurs in a stage known as data visualization. Here is how it can impact your marketing strategy.

#7. Determining Product Fit

Developing a scope on customer pain points and market needs becomes far easier when armed with the demographic, behavioral, and psychological data of your customers.

Figure out which features your customers leverage most and listen to your customer feedback (particularly from those who churn) to help determine where needs exist.

#8. Analyzing Optimal Campaign Channels and Content

Of course, all of this activity feeds back toward future campaign design. As new customers enter your pipeline, leverage that data to analyze the most appropriate channels, types of content, and even date and time to target specific audiences.

Conclusion

These eight strategies have the potential to transform your marketing strategy. However, do not forget that they all hinge on clean, quality data. Without that piece of the puzzle in place, predictive analytics can actually have a negative impact on your business.

The good news is, ReachForce is here to help. ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean, and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.

 

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