5 Strategies From Top Firms on How to Use Machine Learning

With machine learning making disruptive innovation easier than ever before, it's up to entrepreneurs to show the big kids how it's done.

Vince Lynch

Machine learning is headed for a major growth spurt. After ticking past the $1 billion mark in 2016, the machine learning market is expected to hit $39.98 billion by 2025, according to a new report by Research and Markets. 

Where will all that growth come from? Everywhere! Machine learning was born in 1959, coined by computer scientist Arthur Samuel -- but only recently has the larger business community come to understand its value. In the next few years, it will be adopted by everyone from Fortune 500 firms to mom-and-pop shops.

Of course, the first challenge of machine learning is identifying a use case. Not sure where to start? To make the most of this explosive technology, consider how today's top companies, ranging in industry from retail to hardware to media, are using it:

1. Target: Learn from the present to invest in the future.

Retail giant Target discovered that machine learning can be used to predict not only purchase behavior, but also pregnancy. In fact, Target's model is so precise that it can reliably guess which trimester a pregnant woman is in based on what she's bought. After a father discovered through Target's persistent promotions that his 16-year-old daughter was pregnant, Target actually had to dial its initiative back by mixing in less specific ads.

Most companies' promotions are driven by the seasons or holidays. Snow shovels go on sale in July, sunscreen in June. But consumers go through seasons in their own lives, too. The worst time to sell someone a car, for example, is right after she just bought one. It might be the best time, however, to market car insurance to that person. Machine learning can pick up on those rhythms, helping companies recommend their products to customers when the timing is just right.

My company has used machine learning to spur loyalty purchases. We discovered that if a customer is going through a life event (such as graduation or marriage), he is more likely to change his behavior than at other times in his life. An education company that knows 20 percent of its users leave every May, for example, might use machine learning to refer likely grads to a corporate partner or sponsor.

2. Twitter: Create the perfect preview.

When someone posts a photo to Twitter, she wants people to see it. But if the thumbnail is 90 percent floor or wall, nobody is going to click on it. Twitter seems to have solved this problem by using neural networks. In a scalable, cost-effective way, the social media firm is using machine learning to crop users' photos into compelling, low-resolution preview images. The result is fewer thumbnails of doorknobs and more of the funny signs just above them.

Give Twitter's thumbnail optimization a try for your next marketing campaign. Upload brand-aligned, user-generated photos, and let Twitter determine which elements of each image maximize engagement. Then, use the top-performing photo crops for your next Twitter campaign. Who doesn't love free market research? 

We use machine learning to tweak images for conversational engagements. The challenge is making sure that rich images load quickly enough to keep up with a live conversation. A user who sends a question or search request expects a reply in the form of an image or GIF immediately. Using machine learning, we can deliver appropriate responses at scale in seconds.

3. Apple: Embrace ensemble experiences.

Anyone with more than one Apple product knows how well the devices play with one another. Now, the tech giant is using machine learning to create even more seamless customer experiences. Apple recently filed a patent that, in non-technical terms, implies that it's prioritizing cross-device personalization. In the near future, for example, a user's Apple Watch might suggest an iTunes playlist to match his heartbeat goal in another app.

At Cannes Lions 2017, we did something similar (though perhaps less patentable). We hooked our natural language processing system to Foursquare, Google Maps and local knowledge bases to help users navigate the tourist destination and conference. The result was a conversational experience that synthesized data from multiple sources to deliver quick answers.

Any company that works with smart devices, such as an Internet of Things startup, can do this. Connecting multiple models with the same set of training data improves the quality of insights delivered and, thus, the customer's experience. Devices hooked together as ensemble models cooperate like a baseball pitcher and catcher: Because they're working from the same data set, they're able to jointly decide how to approach a task from opposite sides.

4. Alibaba: Customize customer journeys.

A whopping 500 million people shop with Chinese retail giant Alibaba, more than the entire U.S. population. Each of those customers goes through a separate and distinct journey, from searching to buying. How does Alibaba track and tailor each of those 500 million journeys? With machine learning, of course.

Alibaba's AI should make every e-tailer jealous. Its virtual storefronts are customized for each shopper. Search results turn up ideal products. Ali Xiaomi, a conversational bot, handles most spoken and written customer service inquiries. Every element of Alibaba's business feels like it was built for the shopper engaging with it, and every action the shopper takes teaches the machine more about what the shopper wants.

5. Spotify: Deliver personalized media.

After acquiring two machine learning startups in 2017, Spotify, which I used to work with, is quietly testing out new features for its fan-favorite music recommendation service.

Last December, a Mashable writer noticed "like" and "don't like" buttons in her Discover Weekly feed. While Spotify has been tight-lipped about its intentions in acquiring video recommendation startup MightyTV in March 2017 and music personalization startup Niland just two months later, it likely did so to refine its AI stack and outsmart other music services.

Spotify's bet on Discover Weekly speaks to the premium music consumers place on personalization, made possible through its innovative use of machine learning. Even Spotify is surprised by Discover Weekly's success, which wasn't part of the music streaming company's offerings when it launched in 2007. Copycat services like Apple's New Music Mix quickly cropped up after Discover Weekly's 2015 debut, but they've struggled to surprise listeners with recommendations like Spotify's service does.

Of course, machines can't learn everything about a business or its customers. But companies like Apple, Spotify and Alibaba are pushing that boundary back further and further. Now, with machine learning making disruptive innovation easier than ever before, it's up to entrepreneurs to show the big kids how it's done. 

Advanced analytics: A model answer

By James Walker 

More than half of companies that invested over £10m in analytics outperformed their peers – double the proportion of those who invested £4-£10m.

Big data analytics has unleashed a wave of change throughout the business world and is now on the tip of every CEO’s tongue. We often hear about how analytics has transformed outcomes for businesses across sectors; whether it’s delivering ultra-personalised customer services, predicting consumer behaviour, or boosting productivity through automation.

Everyone wants a piece of the action, and companies are investing heavily in the rush not to be left behind. British businesses plan to double their current spending on analytics from £12bn to £24bn by 2020, while in the US it’s a similar story, with investment expected to increase from £58bn to £112bn.

And there is good reason to spend big. Our research, laid out in ‘Putting Analytics to Work’, shows that 52 per cent of companies that invested over £10m in analytics outperformed their peers – double the proportion of those who invested £4-£10m.

But while there is a clear link between the value invested in analytics and overall performance, this doesn’t tell the full story. The reality is that many attempts to build analytics into a business fall flat, with only 20 per cent of companies reporting a significantly positive commercial impact from their efforts. This was the catalyst for our report – what actually are the conditions for a successful analytics capability to flourish?

It may be common knowledge that capitalising on big data has the potential to impart a competitive advantage, but what is less well known is how to actually make analytics successful in the long term. This not only requires significant investment, but also ensuring the right conditions are in place.

Building the foundations

At a foundational level, obtaining support from the top of the organisation is critical. The CEO must champion analytics and drive the various changes needed across the business to make it work. This is reflected in the research, which finds that over half of top performing companies believe their senior leaders support progress in analytics. The patience to remain committed to the transformation over the long term is also essential to prevent fledgling efforts from fizzling out.

And crucially, the development of the analytics capability must be aligned with the overall business goals from day one to ensure analytics is being deployed in the right place to make a commercial impact. One thing analytics should not be is a hammer looking for a nail.

Pillars of success

On top of this foundation sit five imperatives that all contribute to enabling successful analytics. The most critical of these is ensuring the analytics capability is substantial enough to make a tangible impact that can be sustained. Small-scale pilot projects and ‘proof of concepts’ will quickly wither and die without the oxygen of broad support and adoption. Almost 70 per cent of the best performing companies have a centralised analytics capability, which is better placed to address problems from across the business and inform more pivotal strategic decisions.

In addition to having a central hub, analytics is most successful when it’s embedded into the organisation’s entire eco-system. That means all departments must be willing to change their processes to maximise analytics input, which in turn helps to identify the key pivot points where analytics can make the biggest difference.

For many people, the most obvious enabler of analytics is perhaps technology, which is often also the main trigger for investment; for example, when a company digitalises customer services. Technological applications – such as automating manual processes - also help embed analytics into an organisation. But technology isn’t much use if you’re not using the right data, which is why data quality is another key pillar of analytics success.

Ultimately, the analytics capability must continuously evolve over time to reflect wider changes taking place across the business and operating environment. A regular feedback and review process therefore has a key role to play in making sure analytics continues to boost the bottom line.  

Empowering people in a data-friendly culture

All of these elements must be woven into a culture that is informed about the potential of data, that encourages discovery and trust, and that ultimately embraces analytics as an integral part of everyday work. Our research confirms this, with about 80 per cent of meetings in the best performing companies including analytics or data elements.

Furthermore, both the providers and consumers of analytics must be informed about the value of analytics and how to take advantage of it. This means training the analysts about what the business needs, and training the business about how to benefit from the analysis.

From a talent standpoint, the analysts themselves must have a deep understanding of the commercial challenges facing the business and the ability to structure a technical solution accordingly. Our research suggests this is the real talent-related challenge for businesses trying to leverage big data analytics, as opposed to a global ‘drought’ of data scientists, which is often thought to be the problem. 

These imperatives also align with a general preference among companies to employ a pool of specialised people who are focused on tackling key questions, rather than equipping everyone with self-service analytics tools.

Considerations for business leaders

Advanced analytics is clearly here to stay. It sharpens the competitive edge for the best performers across the value chain and boosts sales and profits for those who adopt it. And as investment in analytics grows across sectors, the gap between the top and poor performers will only widen.

But investing money in analytics without adhering to at least some of the key conditions for success, such as having senior backing and changing culture, is likely to yield poor results.

CEOs embarking on a mission to build or expand their organisation’s analytics capabilities should start by asking their teams some important questions, including:

·         How can we improve the analytics capability given what we have available?
·         Which of the 10 imperatives should we focus on and in what order?
·         Which aspects can we accelerate with external support?

Finding the answers to those questions and allocating the right level of investment will help pave the way for success with analytics and for the organisation as a whole. 

Ten Ways Big Data Is Revolutionizing Marketing And Sales

• Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) & Enterprise Data Warehouse Optimization (10%) are among the most popular big data use cases in sales and marketing.
• Customer Value Analytics (CVA) based on Big Data is making it possible for leading marketers to deliver consistent omnichannel customer experiences across all channels.

Of the hundreds of areas big data and analytics will revolutionize marketing and sales, the following is an overview of those that are delivering results today. How prices are defined, managed, propagated through selling networks and optimized is an area seeing rapid gains.  Attaining price optimization for a given product or service is becoming more possible thanks to advances in big data algorithms and advanced analytics techniques. Streamlining routine pricing decisions in commodity-driven industries where products are inelastic is also happening today.

An Overview Of Big Data’s Many Contributions To Marketing And Sales

Increasing the quality of sales leads, improving the quality of sales lead data, improving prospecting list accuracy, territory planning, win rates and decision maker engagement strategies are all areas where big data is making a contribution to sales today.

In marketing, big data is providing insights into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management (CRM) systems can be improved, in addition to strategies for increasing conversion rates, prospect engagement, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage many other customer-driven metrics essential to running a cloud-based business.

The following are the ten ways Big Data is revolutionizing marketing and sales:

1. Differentiating pricing strategies at the customer-product level and optimizing pricing using big data are becoming more achievable. McKinsey found that 75% of a typical company’s revenue comes from its standard products and that 30% of the thousands of pricing decisions companies make every year fail to deliver the best price. With a 1% price increase translating into an 8.7% increase in operating profits, assuming there is no loss of volume, pricing has significant upside potential for improving profitability.  Source:Using big data to make better pricing decisions.

2. Big data is revolutionizing how companies attain greater customer responsiveness and gain greater customer insights. A Forrester study found that 44% of B2C marketers are using big data and analytics to improve responsiveness to 36% are actively using analytics and data mining to gain greater insights to plan more relationship-driven strategies. Source: Marketing’s Big Leap Forward Overcome The Urgent Challenge To Improve Customer Experience And Marketing Performance (PDF).

3. Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) and Enterprise Data Warehouse Optimization (10%) are among the most popular big data use cases in sales and marketing. A recent study by DataMeer found customer analytics dominate big data use in sales and marketing departments, supporting the four key strategies of increasing customer acquisition, reducing customer churn, increasing revenue per customer and improving existing products. Source: Big Data: A Competitive Weapon For The Enterprise

4. Supported by Big Data and its affiliated technologies, it’s now possible to embed intelligence into contextual marketing. The marketing platform stack in many companies is growing fast based on evolving customer, sales, service and channel needs not met with existing systems today. As a result, many marketing stacks aren’t completely integrated at the data and process levels.  Big data analytics provides the foundation for creating scalable Systems of Insight to help alleviate this problem.  The following graphic is from the Forrester study made available for free download on the SAS site, Combine Systems Of Insight And Engagement For Contextual Marketing Tools And Technology: The Enterprise Marketing Technology Playbook.

5. Forrester found that big data analytics increases marketers’ ability to get beyond campaign execution and focus on how to make customer relationships more successful.By using big data analytics to define and guide customer development, marketers increase the potential of creating greater customer loyalty and improving customer lifetime. The following graphic is from the SAS-sponsored Forrester study How Analytics Drives Customer Life-Cycle Management Vision: The Customer Analytics Playbook (PDF).

6. Optimizing selling strategies and go-to-market plans using geoanalytics are starting to happen in the biopharma industry. McKinsey found that biopharma companies typically spend 20% to 30% of their revenues on selling, general, and administrative If these companies could more accurately align their selling and go-to-market strategies with regions and territories that had the greatest sales potential, go-to-market costs would be immediately reduced. Source:  Making Big Data Work: Biopharma, McKInsey & Company.

7. 58% of Chief Marketing Officers (CMOs) say search engine optimization (SEO) and marketing, email marketing, and mobile is where big data is having the largest impact on their marketing programs today. 54% believe that Big Data and analytics will be essential to their marketing strategy over the long-term. Source: Big Data and the CMO: What’s Changing for Marketing Leadership?

8. Market leaders in ten industries Forbes Insights tracked in a recent survey are gaining greater customer engagement and customer loyalty through the use of advanced analytics and Big Data. The study found that across ten industries, department-specific analytics and Big Data expertise were sufficient to get strategies off the ground and successful; enterprise-wide expertise and massive culture change was accomplished after pilot programs delivered positive results. Source: Forbes Insights, The Rise of The New Marketing Organization.

9. Big Data is enabling enterprises to gain greater insights and actionable intelligence into each of the key drivers of their business. Generating revenue, reducing costs and reducing working capital are three core areas where Big Data is delivering business value today.  An enterprises’ value drivers scale more efficiently when managed using advanced analytics and Big Data.  The following value tree or roadmap to value illustrates this point.  Source: Big Data Stats from Deloitte.

10. Customer Value Analytics (CVA) based on Big Data is making it possible for leading marketers to deliver consistent omnichannel customer experiences across all channels.CVA is emerging as a viable series of Big Data-based technologies that accelerate sales cycles while retaining and scaling the personalized nature of customer relationships. The bottom line is that CVA is now a viable series of technologies for orchestrating excellent omnichannel customer experiences across a selling network. Source:   CapGemini Presentation, From Customer Insights to Action Ruurd Dam, November 2015.