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Find New Customers But Keep The Old – One Is Silver And The Other Is Gold

Michael Brenner

Sometimes browsing the latest trends in marketing can be like walking through a carnival. There are plenty of half-baked ideas, and digital obsessions beefed up with content marketing lingo like an MMA fighter on steroids.

But not the meaty answers that you seek.

Do you feel like this? Then it may be time to get back to the fundamentals. Time to forget about the shiny objects for a minute. Ditch the latest tricks. At least for today.

It’s time to look to where marketing’s purest truths can be found. . .

Childhood nursery rhymes.

Make new friends, but keep the old. One is silver, and the other gold.

Are you spending enough energy on retaining your existing customer base, your true gold?

Or are you focusing your content marketing strategies on new leads, the less-valuable yet shimmering silver target?

Don’t neglect your gold customers

It is time to make churn rate and customer retention a priority again. Churning is when you lose your existing customers. A low churn rate means you are able to keep most of the customers that your company does business with.

Your job is to get that churn rate down as much as possible. That means customers are not:

  • Closing their accounts
  • Choosing to purchase from a competitor
  • Canceling their subscriptions
  • Not renewing their service agreements

After all, it is well known that it is astronomically more expensive to find new customers than it is to market to past ones. It can cost from 5 to 25 times more to win over that new lead than to keep an old one, which is why customer retention and reducing churn is a central focus for leading marketers.

Old customers will create the most new business

Not only is it more expensive to chase new leads, but your loyal customers are the ones who are probably going to be behind future revenue growth. Many of today’s CEOs are putting more pressure on CMOs to include revenue growth on their already lengthy to-do lists. Need to prove your marketing worth through revenue growth?

No problem. Your customer base can help.

Think of your loyal customers as your Praetorian Guard. They are there for you. They’ll make sure you are victorious each day. According to research agency Gartner Group, a hefty chunk of a company’s future revenue growth, about 80%, will come from a modest and dependable 20% portion of their existing customers.

Just make sure you keep them happy.

Loyal customers deserve the gold treatment

How do you retain your customers? Simple: Treat them like the gold that they are.

1. Create a loyalty program that makes them feel special.

To keep your loyal customers loyal, let them eat cake! Offer a simple, straightforward rewards program that truly adds value. Think Virgin Atlantic’s Flying Club classic tier-based program, or Sephora’s exclusive Beauty Insider program, which offers customers more than deals. Beauty aficionados can get their hands on limited-edition sets and free samples – prized swag that those pricey new leads don’t have access to.

2. Listen.

Retaining customers is a lot like working to maintain a healthy relationship. Aside from letting them know how special they are, marketers also need to do something that may take practice: Listen. Read the feedback posted on social media channels, reviews, and in customer emails. What are people saying about the products or services you are trying to sell? What are the subtle messages behind the politeness or frustration?

The more you pay attention to the good and the bad, the more you can learn about your existing customers and create those insightful buyer personas so you can master the next method for getting your churn rate down.

3. Communicate like you know who you are talking to.

This is where you need to find the perfect balance between marketing automation and personalized communication. To make consumers feel like they matter, like they are being directly reached out to by their favorite brands, you’ve got to speak to them.

The best marketers don’t reach out to customer X. They send an email to Marcia LovesYourServices. She is a woman in her mid 40’s who doesn’t have a lot of free time. But she does enjoy treating herself to your service/product once every couple of months.

Or Fred Can’tGetEnoughofYourProduct, who is an avid industry enthusiast and appreciates being kept up-to-date with the latest industry news.

4. Chase your churners.

You’ll get more value from following up with the customers you lose than you will from finding new customers to replace your churners. If someone cancels, switches to a competitor, or closes their account, politely and respectively find out why. You can use this feedback. It can help you prevent your churn rate from growing by showing you how to make sure others don’t follow suit.

Maybe a new competitor popped up and is offering a better deal. Are some customers finding a problem with your product?

These are gems of information that marketers need to be aware of. All of a business’s lost customers may not feel inclined to respond. Some, however, will be happy to give away their two cents.

Old customers can find you new customers

Another advantage of fortifying the interest and loyalty of your existing customers is the referral phenomena.

Repeat customers who derive value from what a business is selling are your best marketers. Through their raving online reviews, social shares, and word-of-mouth, they are capable of much more than their purchases alone.

In fact, word-of-mouth is known to be the driving force behind between 20% and 50% of purchasing choices.

Marketers who want record-breaking ROI need to balance old and new, with digital marketing ingenuity. It is well worth your time to focus on making sure your existing customers are happy, finding out if and how their expectations are changing, and letting them know that they are valued.

Customer retention is a foundational pillar of successful marketing. It will attract revenue like a magnet.

Make sure your loyal customer base gets its fair share of your marketing efforts, and make this an ongoing process. Then, go ahead and explore the latest trends in digital and content marketing.

For more customer retention strategies, see Customer Retention: The Lost Art (And Science) Of Marketing.

Image: pixabay

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About Michael Brenner

Michael Brenner is a globally-recognized keynote speaker, author of  The Content Formula and the CEO of Marketing Insider GroupHe has worked in leadership positions in sales and marketing for global brands like SAP and Nielsen, as well as for thriving startups. Today, Michael shares his passion on leadership and marketing strategies that deliver customer value and business impact. He is recognized by the Huffington Post as a Top Business Keynote Speaker and   a top  CMO influencer by Forbes.

Amazing Digital Marketing Trends And Tips To Expand Your Business In 2015

Sunny Popali

Amazing Digital Marketing Trends & Tips To Expand Your Business In 2015The fast-paced world of digital marketing is changing too quickly for most companies to adapt. But staying up to date with the latest industry trends is imperative for anyone involved with expanding a business.

Here are five trends that have shaped the industry this year and that will become more important as we move forward:

  1. Email marketing will need to become smarter

Whether you like it or not, email is the most ubiquitous tool online. Everyone has it, and utilizing it properly can push your marketing ahead of your rivals. Because business use of email is still very widespread, you need to get smarter about email marketing in order to fully realize your business’s marketing strategy. Luckily, there are a number of tools that can help you market more effectively, such as Mailchimp.

  1. Content marketing will become integrated and more valuable

Content is king, and it seems to be getting more important every day. Google and other search engines are focusing more on the content you create as the potential of the online world as marketing tool becomes apparent. Now there seems to be a push for current, relevant content that you can use for your services and promote your business.

Staying fresh with the content you provide is almost as important as ensuring high-quality content. Customers will pay more attention if your content is relevant and timely.

  1. Mobile assets and paid social media are more important than ever

It’s no secret that mobile is key to your marketing efforts. More mobile devices are sold and more people are reading content on mobile screens than ever before, so it is crucial to your overall strategy to have mobile marketing expertise on your team. London-based Abacus Marketing agrees that mobile marketing could overtake desktop website marketing in just a few years.

  1. Big Data for personalization plays a key role

Marketers are increasingly using Big Data to get their brand message out to the public in a more personalized format. One obvious example is Google Trend analysis, a highly useful tool that marketing experts use to obtain the latest on what is trending around the world. You can — and should — use it in your business marketing efforts. Big Data will also let you offer specific content to buyers who are more likely to look for certain items, for example, and offer personalized deals to specific groups of within your customer base. Other tools, which until recently were the stuff of science fiction, are also available that let you do things like use predictive analysis to score leads.

  1. Visual media matters

A picture really is worth a thousand words, as the saying goes, and nobody can deny the effectiveness of a well-designed infographic. In fact, some studies suggest that Millennials are particularly attracted to content with great visuals. Animated gifs and colorful bar graphs have even found their way into heavy-duty financial reports, so why not give them a try in your business marketing efforts?

A few more tips:

  • Always keep your content relevant and current to attract the attention of your target audience.
  • Always keep all your social media and public accounts fresh. Don’t use old content or outdated pictures in any public forum.
  • Your reviews are a proxy for your online reputation, so pay careful attention to them.
  • Much online content is being consumed on mobile now, so focus specifically on the design and usability of your mobile apps.
  • Online marketing is essentially geared towards getting more traffic onto your site. The more people visit, the better your chances of increasing sales.

Want more insight on how digital marketing is evolving? See Shutterstock Report: The Face Of Marketing Is Changing — And It Doesn’t Include Vince Vaughn.

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About Sunny Popali

Sunny Popali is SEO Director at www.tempocreative.com. Tempo Creative is a Phoenix inbound marketing company that has served over 700 clients since 2001. Tempos team specializes in digital and internet marketing services including web design, SEO, social media and strategy.

Social Media Matters: 6 Content And Social Media Trend Predictions For 2016 [INFOGRAPHIC]

Julie Ellis

As 2015 winds down, it’s time to look forward to 2016 and explore the social media and content marketing trends that will impact marketing strategies over the next 15 months or so.

Some of the upcoming trends simply indicate an intensification of current trends, however others indicate that there are new things that will have a big impact in 2016.

Take a look at a few trends that should definitely factor in your planning for 2016.

1. SEO will focus more on social media platforms and less on search engines

Clearly Google is going nowhere. In fact, in 2016 Google’s word will still essentially be law when it comes to search engine optimization.

However, in 2016 there will be some changes in SEO. Many of these changes will be due to the fact that users are increasingly searching for products and services directly from websites such as Facebook, Pinterest, and YouTube.

There are two reasons for this shift in customer habits:

  • Customers are relying more and more on customer comments, feedback, and reviews before making purchasing decisions. This means that they are most likely to search directly on platforms where they can find that information.
  • Customers who are seeking information about products and services feel that video- and image-based content is more trustworthy.

2. The need to optimize for mobile and touchscreens will intensify

Consumers are using their mobile devices and tablets for the following tasks at a sharply increasing rate:

  • Sending and receiving emails and messages
  • Making purchases
  • Researching products and services
  • Watching videos
  • Reading or writing reviews and comments
  • Obtaining driving directions and using navigation apps
  • Visiting news and entertainment websites
  • Using social media

Most marketers would be hard-pressed to look at this list and see any case for continuing to avoid mobile and touchscreen optimization. Yet, for some reason many companies still see mobile optimization as something that is nice to do, but not urgent.

This lack of a sense of urgency seemingly ignores the fact that more than 80% of the highest growing group of consumers indicate that it is highly important that retailers provide mobile apps that work well. According to the same study, nearly 90% of Millennials believe that there are a large number of websites that have not done a very good job of optimizing for mobile.

3. Content marketing will move to edgier social media platforms

Platforms such as Instagram and Snapchat weren’t considered to be valid targets for mainstream content marketing efforts until now.

This is because they were considered to be too unproven and too “on the fringe” to warrant the time and marketing budget investments, when platforms such as Facebook and YouTube were so popular and had proven track records when it came to content marketing opportunity and success.

However, now that Instagram is enjoying such tremendous growth, and is opening up advertising opportunities to businesses beyond its brand partners, it (along with other platforms) will be seen as more and more viable in 2016.

4. Facebook will remain a strong player, but the demographic of the average user will age

In 2016, Facebook will likely remain the flagship social media website when it comes to sharing and promoting content, engaging with customers, and increasing Internet recognition.

However, it will become less and less possible to ignore the fact that younger consumers are moving away from the platform as their primary source of online social interaction and content consumption. Some companies may be able to maintain status quo for 2016 without feeling any negative impacts.

However, others may need to rethink their content marketing strategies for 2016 to take these shifts into account. Depending on their branding and the products or services that they offer, some companies may be able to profit from these changes by customizing the content that they promote on Facebook for an older demographic.

5. Content production must reflect quality and variety

  • Both B2B and B2C buyers value video based content over text based content.
  • While some curated content is a good thing, consumers believe that custom content is an indication that a company wishes to create a relationship with them.
  • The great majority of these same consumers report that customized content is useful for them.
  • B2B customers prefer learning about products and services through content as opposed to paid advertising.
  • Consumers believe that videos are more trustworthy forms of content than text.

Here is a great infographic depicting the importance of video in content marketing efforts:
Small Business Video infographic

A final, very important thing to note when considering content trends for 2016 is the decreasing value of the keyword as a way of optimizing content. In fact, in an effort to crack down on keyword stuffing, Google’s optimization rules have been updated to to kick offending sites out of prime SERP positions.

6. Oculus Rift will create significant changes in customer engagement

Oculus Rift is not likely to offer much to marketers in 2016. After all, it isn’t expected to ship to consumers until the first quarter. However, what Oculus Rift will do is influence the decisions that marketers make when it comes to creating customer interaction.

For example, companies that have not yet embraced storytelling may want to make 2016 the year that they do just that, because later in 2016 Oculus Rift may be the platform that their competitors will be using to tell stories while giving consumers a 360-degree vantage point.

For a deeper dive on engaging with customers through storytelling, see Brand Storytelling: Where Humanity Takes Center Stage.

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About Julie Ellis

Julie Ellis – marketer and professional blogger, writes about social media, education, self-improvement, marketing and psychology. To contact Julie follow her on Twitter or LinkedIn.

Data Lakes: Deep Insights

Timo Elliott, John Schitka, Michael Eacrett, and Carolyn Marsan

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

Data lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.

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Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.