The Omnichannel News Roundup: Week of November 2, 2015
Here are some of the articles we’ve been reading this week:
This week’s featured article: How understanding the limitations of Big Data analytics will actually help you make more informed decisions and become bolder innovators.
Though personalization software can automatically give marketers a more complete picture of their customers, CMOs still play a crucial role in balancing the technology’s scale with its relevance. This article explores some of the questions businesses may encounter in their personalization journey – such as how to treat “dark” social data – and gives insight from a unique perspective.
PepsiCo exec Brad Jakeman went off on advertisers, criticizing them for unoriginality, outdated ways of thinking based on the dying TV model, and for an overabundance of “white straight males.”
Making use of Big Data is a staple of every digital marketer’s playbook, especially those in the retail sector who will use it to measure such things as transaction and customer loyalty. There is a lot of potential to be found in Big Data analytics, but it’s lost when retailers grow too comfortable with a “proven” approach and fail to keep testing and discovering new viable marketing strategies. Ultimately, realizing the limits of Big Data analytics and becoming comfortable with experimentation is the key to making sure your business stays in touch with the demands of its environment and its customers.
Analytics are Impressive, but Incomplete
Current trade promotion optimization (TPO) solutions simply weren’t designed to extract signal from the large volume of noisy data that modern retailers generate, and analytics is still unable to specifically measure the effect of qualitative factors like ad design, layout, and wording on ROI. This means that, for better or worse, the current limitations of our technology mean that Big Data can only give us a partial look at what worked and what did not.
Don’t get me wrong. Data analytics provides marketers with an incredible degree of insight, but we must remember to use it as a toolwith defined goals and a good understanding of what it can and cannot accomplish.
For instance, analytics is much better at explaining the past than predicting the future. This means that you can’t predict with certainty the effect that a certain kind of discount, pricing structure, or cross-merchandising tactic will play out: you need to run an experiment. This can be scary, but an unwillingness to try new things could leave your business wading in the shallow end of success, perhaps losing very little but not making much either.
Offer Innovation: A Better Way
As a rule, the best way to experiment in this field is to maximize your exposure to positive (beneficial) risks while minimizing your exposure to negative risk. The best way to do so in the omnichannel age is to experiment with offer innovation, a series of micro-tests that you can use to analyze the small-scale performance of diverse offers and messages across channels. Instead of shifting your entire marketing campaign in one great, lurching motion, you’ll be testing many ideas at once and keeping an eye on any promising developments without risking the bulk of your business. The gamble you’re taking is absorbing little failures (i.e., a particular method falls flat) in hopes that you’ll stumble upon a new, reliable way to connect with a certain kind of customer.
Ironically, realizing the limitations of data analytics will help you make better decisions and make more calculated risks. Don’t be afraid to make small mistakes, especially when there’s a large potential payoff at the end.
When people think big data, certain industries come to mind. Government, retail, health care and financial services top the list of enterprises collecting and capitalizing on customer data.
But as personalization and omnichannel have become more of an expectation across all markets, new industries have started using customer data with the goal of improving a consumer’s experience.
One industry in particular stands out because of recent data collection and management advances. Through smartphone apps and fitness gadgets, the health and wellness industry is using data to revamp and refuel sales.
Why should we care what the health industry does?
The health and wellness industry holds high status in the marketplace. With Nike named the top brand for the largest purchasing power, we can expect Nike and other wellness enterprises to stay relevant in the market by incorporating the latest technology into marketing strategy. Based off these innovations, companies in any industry can get inspired by these groundbreaking new ways to use data.
So how is the health and wellness industry using customer data?
Nike+ Running lets users track their workouts…and their friends’ too.
Smartphone Apps
Smartphone apps that focus on health have been around for years. These apps were the original building blocks for exposing health and fitness to data. Because of their initial influence, apps have a key role in the health industry’s data interest.
The most popular smartphone health apps come in the form of tracking and managing workouts. Smartphone apps like Nike+ Running record and store data from a workout. The app measures various elements of a workout, including distance covered, calories burned, average pace, and duration of workout.
These are successful because they let users access and manage their data easily. A major motivator for fitness gurus is tracking and viewing progress, which can be easily done through a simple download on a smartphone.
And some apps offer more than just tracking data. Nike+ Running can sync to social media accounts and notify Facebook friends about big accomplishments, like longest run or fastest pace. With this multi-channel development, smartphone apps are getting praise across a variety of platforms.
Fitness wearables
Smartphone apps typically only collect data during a workout. However, technological advances are helping fitness fanatics track their health 24/7.
Wearable devices help consumers manage their health with an in-depth, convenient approach. They come in different varieties and forms – from the Fitbit to watches to diamond crested accessories. These fitness devices measure specific elements of health, such as steps counted or hours slept. The device then processes the information into a consumer’s personal profile, which the consumer can manage at his or her leisure.
In today’s market, there is no doubt about the potential for these fitness appliances. The only debate in this arena is over which wearable is best.
With the ability to constantly track activity, sleep, heart rate, calories and location, fitness wearables are convenient and easy answer to a healthier lifestyle.
Specialized omnichannel gadgets
Data usage is not limited to fitness tech – personal hygiene is using consumer data as well.
Personal hygiene may seem like an unlikely candidate for data usage. However, the dental industry is starting to focus on personalization and omnichannel, placing a need for customer data.
Beam Brush lets users track their toothbrushing behaviors. The program offers its users special rewards and loyalty programs.
In a recent AdAge article, writer Kate Kaye explores an innovation that is redefining dental hygiene. Beam Brush is a toothbrush-inspired enterprise. It connects its users to a network of 95,000 dentists and discounts based on points awarded after using the brush. Users track their teeth cleaning activity and are rewarded with loyalty programs. All activity is synchronized to a user profile in a mobile device.
Despite its ties to teeth, Beam Brush emphasizes that it is not a toothbrush company. Instead, Beam Brush is more invested in collecting health data.
Beam Technologies founder and CEO Alex Frommeyer reportedly said, “If we know there are a million people in Beam’s ecosystem and we know what behavioral triggers we tend to see with high rates of gum disease, then that insight can be translated to a dentist when we see those triggers hit.”
This insight Frommeyer references can prevent dental damage and increase loyalty programs. This is extremely powerful for companies, as an increase in loyalty likely results in improved ROI.
Takeaways
Besides getting inspired from learning about the latest and greatest ways that unlikely vendors are using data, there are some key things marketers should recognize from these recent advancements.
First, it’s important to recognize that if one unlikely (but large) industry is using customer data, several others are sure to follow. This means that more businesses will utilize customer data. With more entities capitalizing on data, consumers will likely be pressed to share more personal info. With more data readily available, marketers should consider the potential for third-party data integration.
Another important takeaway is to acknowledge how willingly these users share personal data with these devices. The reason some users are letting their movements be tracked 24/7 (read: FitBit) is because they feel safe and secure in their data protection. Marketers should realize that, when customers feel like their data will be safely guarded, they will share more.
The final takeaway is that that there is so much more opportunity for customer data usage. With this notable amount of data integration in a short span of time, marketers must anticipate and prepare for upcoming technology innovations and trends. Marketers should stay informed via tech news outlets, to ensure they don’t get left behind in data usage.
With the increasing integration of technology and data in sales, there has never been a better time to use location intelligence in marketing. Using location data is too easy and too beneficial to ignore. Location features help businesses stand out amongst competitors and are a staple for today’s leaders in marketing. As the concept grows, customers are beginning to expect it when using technology in their shopping experience.
Location intelligence is a must for companies that want to be successful. Read on to find out why.
It’s too easy
Working with all types of data is easy, and location data is no exception.
With a quality Data Management Platform, businesses can store, manage, organize and analyze their data in one entity. Because companies are utilizing DMPs more than ever, there is now an abundance of platforms in the marketplace ranging in price and features. With several types of platforms on the market, companies are able to choose the DMP that best suits their needs in terms of performance ability and price.
The right DMP makes data easy to use and understand. And finding the right DMP is too easy for companies not to utilize.
It’s an asset to businesses
When companies think “big data” there are certain metrics that come to mind. Typically, businesses look at what is being purchased or who is doing the purchasing.
Amazon Local customizes its deals based on customers’ location
Location data is often underrated, but just as important.
Location data gives insight to consumer behavior based on a key demographic. When analyzed, this data shows geographic patterns and trends. Companies can use this information to segment their audience. These segments can then be targeted with personalized messages specifically designed to their wants and needs.
One company that utilizes the power of location data is Amazon. Through their Amazon Local feature, the company sends its consumers deals they may be interested in based on their location. For example, emails that I receive are based on my home address and contain offers just a few miles away from my house. All offers show how far away the brick-and-mortars are, which can be helpful for consumers who don’t want to make a far drive to use their deal.
It’s a customer favorite
If there’s anyone who values location data more than businesses, it’s their audience.
As businesses are beginning to include location data into their services, customers have become exposed to new interactive features that rely on location data.
One of the most loved features that uses location data includes a GPS element that shows shoppers where the nearest store location is. This feature is often available on apps or websites. Some companies have expounded on this ability by adding in-store pickup options for customers when they checkout online.
The ability to purchase online and pick up in-store is not universally used by all retailers – at least, not yet. A Forrester report says that 50% of consumers expect the option to buy online and pick up in-store. As companies gradually rely more on more on technology in marketing, this already large fraction will likely increase. Companies who have not implemented location services like this need to seriously consider doing so; otherwise, they risk being left behind with the technologically-impaired.
Walmart’s blog shows its app, Shop My Store, which lets customers find items in the brick-and-mortar.
Consumers also love retailers that incorporate item locators into their business strategy. Large stores like Walmart can often be overwhelming. However, by introducing an app that shows an item’s availability and aisle location, Walmart turns a headache-inducing shopping experience into a 5-minute spree. Employees have commented that the feature, in some cases, has actually saved sales by making the shopping experience easier and more convenient for the customer.
Services that incorporate location data work because customers love them. Features with location intelligence make shopping experiences more convenient. And, because an easy shopping experience correlates with a powerful shopping experience, customers prefer marketing that considers location data.
April Fool’s is around the corner…and nectarOM has a few suggestions to reduce your risk of getting fooled by data analysis.
In a time when data is abundant and necessary for a strong personalized marketing strategy, marketers should be on the look out for these most common ways that data is misinterpreted. The following are some mistakes commonly made in data analysis.
Causation and Correlation
Understanding the difference between causation and correlation is important to interpreting data. Because both concepts sound relatively similar and are related to statistics, they are easily confused for one another.
Causation occurs when one event causes another. For example, as summer approaches, a swimwear retailer may see an increase in sales as more people buy swimsuits.
Correlation occurs when there is a mutual relation between two events. However, one of these events does not necessarily need to cause the other. For example, ice cream sales may increase and a swimwear retailer’s sales may increase, however, this does not mean that the increase in ice cream sales causes people to buy more swimwear. In this case, the rise in temperature is the cause of both of these events.
Understanding the difference between causation and correlation is important to avoid an incorrect data analysis. If the aforementioned swimwear retailer confuses its correlation and causation with ice cream sales, the retailer may see problems arise if it adjusts its marketing campaign to reflect the success of ice cream sales. For example, if summer ice cream sales increase because a neighboring frozen yogurt shop shuts down, the swimwear retailer may wrongfully assume their sales will increase as well. This assumption, which is not necessarily correct, could contribute to an ineffective marketing campaign.
Using Old Data
When a company is stuck with outdated customer information, its data may become useless. For example, a company may be sending emails to a customer’s old email address. If this customer no longer checks this email address, he or she will not have the opportunity to open emails from companies they might have registered with. This could alter the company’s email open rates. Several cases of this could lead to incorrect assumptions of ineffective subject lines or poor sending times based off faulty data collected in the scenario. To prevent these types of inaccurate assumptions, analysts should ensure they are using current customer information and use business rules to exclude customers who have not opened within a certain period of time.
Assuming the Data Will Do it All
One of the attractive selling points of using a Data Management Platform is that it reduces work for marketers and analysts. However, this mindset is a slippery slope. Companies should make sure their staff knows that a DMP doesn’t mean no more work in personalizing and customer care. Marketers can simply sit back and let their DMP run their data analysis and marketing campaigns. Marketers must remain attentive and responsive to consumer behavior, ensuring that marketing does not take on a robotic, impersonal feel.
Measuring the Average
When determining metrics in a data set, marketers must determine how to measure an accurate average. In some data sets, using mean versus median can present some vastly different results.
Mean accounts a total of all values added, then divided by the amount of data points. Median is the exact middle of the data set in numerical order. In cases where there are extreme outliers, using the median can give analysts a better picture of an average.
Oftentimes, the median gives marketers a more accurate look at its average. For example, consider a retailer’s data that tracks how long visitors stay on their eCommerce site. Imagine a retailer’s data shows that nine users spend 3 minutes on its site, while one user spends 45 minutes on the site. In this scenario, the mean average is 7.2 minutes spent on site, while the median is 3 minutes on site.
The median is a better value for the retailer’s average because it shows reflects a value that is close to what most site visitors showed. In contrast, the mean average reflects a value significantly higher than what 9 out of the 10 visitors generated. The mean’s higher value is skewed by one user’s unusually high value of 45 minutes. This lone value seriously alters the average time spent on the site.
Acknowledging outside factors
Oftentimes, marketers are so focused on the numbers that they forget to account for outside factors that might influence their customer data. For example, when looking at open rates in an automated email campaign, marketers should be sure to consider a customer’s geographical location.
While geographical location might seem irrelevant when sending emails, time zones and time of distribution can significantly impact open rates. Studies show that most consumers open emails from retailers between 2 p.m. and 5 p.m.
If all emails are distributed at the same time, a person in California might receive the message at an optimal time of 4 p.m., while a recipient in New York would receive the same exact message at their time of 7 p.m. While this delivery time is great for the Californian, the New Yorker may be in the middle of dinner and too distracted to open an email. Content should be delivered with the recipient’s location – and time zone – in mind.
Consider Kate Spade’s automated email campaign, which always considers the shopper’s time zone when delivering emails. The women’s clothing brand asks its registrants for two items of information upon signing up for an account: their email and zip code. With this information, Kate Spade emails customers according to their different time zones.
The email to the left is registered with under my California zip code, while the email to the right was registered under my Texas zip code. I received both emails two hours apart – a perfect example of a company accounting for time zone differences.
While data analysis mistakes are bad for marketers, poor data management can be detrimental to a company’s growth and sales as well. Make sure your company’s data analysis and data management are up-to-date and set up for success when implementing data into your marketing campaign.
It’s 2015, and companies are finally getting the hang of data.
Big data has been around for years so it’s about time! More and more companies are using data to profile their customers to generate relevant products and marketing strategies. A study by Forbes and Teradata found that 78% of marketers are incorporating data into their marketing.
As companies explore the possibilities behind data analytics, we have noticed a few trends in the datasphere. Companies that choose to incorporate these trends in their marketing strategies may notice an increase in ROI and an edge over their competitors.
Unsiloing data
Companies are beginning to break down barriers in data sharing. Un-siloing data allows different departments to combine different data sets. A company’s entire stock of customer information may be stored in one simple Data Management Platform. This storage system makes finding, organizing and sharing information an easier, more efficient process.
For example, a company’s IT department may have an impressive set of internal customer information. That same company’s marketing department may have a large collection of CRM data. Companies that unsilo data pool together both sets of information, creating a single in-depth consolidation of data.
The larger data pool is beneficial to both IT and marketers, as the departments will have access to a more complete profile of their customers. This will help both departments provide a more accurate understanding of their customers, generating a more personalized, relevant shopping experience.
Privacy
Over the past year, information security and privacy has been a growing concern amongst the public. With big security breaches like the Sony hack and the iCloud celebrity photo scandal, privacy is a growing concern for many.
In response to public concern about security, companies must employ data protection and safeguarding into their data management. Companies that cannot confidently ensure that a person’s private information will stay private, the likelihood of data sharing will decrease.
Nordstrom clearly outlines its privacy policy to build trust between buyers and itself.
Additionally, easy-to-access privacy policies can increase trust between customers and a company. Provide an in-depth statement informing customers of their privacy rights can increase likelihood of sharing personal information.
Other than putting the public at ease, companies with protective measures in data management will have less risk at security breaches. Data encryption, multiple passwords and security audits may take a little extra and effort. But investing time into safeguarding customer information is vital to prevent big scandals and data breaches.
Data in Real-Time
Companies are beginning to use data in real-time to engage and connect with consumers. In the past, data has been used to create content for consumers, and is set to be viewed at a certain point in time.
Today’s marketers are integrating data and marketing strategies in real-time, to offer content that is even more current and relevant. This real-time marketing is commonly seen across social media platforms.
Nissan UK also creates relevant messages by integrating real-time events into its social media marketing.
Google’s Fifa World Cup campaign is a prime example of real-time data integration. By analyzing Google’s search engine, data experts curated sharable images and facts for its users. This later fueled more discussion about relevant topics for Google users. This real-time analysis helps businesses understand why their target audiences share, engage and spread content.
As data analytics becomes increasingly popular, we expect that companies will employ a real-time data/marketing strategy similar to Google’s. Companies will begin to utilize social media more often, allowing their customers to share, engage and spread content easier.
More Personalization
Personalization has been a hot topic since data was first introduced to the marketing world. And, as more companies grow friendlier with data, feelings toward personalization are sure to follow suit.
Personalization increase also directly correlates with the shift of millennials as the largest purchasing power. As millennials are generally more open to sharing personal information, companies can adjust marketing to incorporate a more personalized, 1:1 marketing feel. A global study by SDL found that 46% of millennials are willing to provide personal information to businesses, in order to get rid of irrelevant marketing.
Netflix – a millennial favorite – personalizes content by providing recommendations based off past interests.
As data continues to evolve, understanding where data is headed can be a big asset to companies. Anticipating changes in data and adjusting strategies accordingly can help your company stand out amongst competitors and remain a consumer favorite.
In every type of industry, data is used to evaluate the status of a concept, product, or idea. From politics to the red carpet, data is used around the world to measure what works, and what doesn’t.
Data is particularly important for those in the marketing world. Data can show whether customers like a company’s marketing, or show companies that their marketing is ineffective.
Data analysis is particularly important for marketers using marketing automation. Because experts believe that the future of marketing automation depends on data, marketers should implement data into their marketing strategy.
Drawing relevant, helpful conclusions from data sets can be difficult. With so many numbers and digits, its easy to get overwhelmed with different rates and percentages. But never fear – NectarOM is ready to make metric evaluation simple. There are certain metrics one should consider when measuring the success of marketing automation.
When evaluating automated email success, marketers should look at several different rates. First, marketers should use a bounce rate to determine how many emails actually get to the intended recipients. Inaccurate email addresses, poor server connection, or full inbox may prevent email subscribers from receiving emails. Establishing why a consumer might not receive emails can be helpful, as marketers can work to fix the bug in their email automation system. Bounce rates distinguish ineffective marketing from inaccessible marketing.
Using a calculated bounce rate, marketers can find the open rate. An open rate is the amount of emails opened as out of the total emails that were delivered to inboxes. Open rates can be useful in determining the attractiveness of a subject line, or the accessibility of a customer through the time the emails were sent.
To determine how successful email content is, marketers should consider click-through rates and conversion rates. Click-through rates reflect the amount of times a link inside of an email was clicked, directing the subscriber to the company website. Conversion rates measure the amount of subscribers who have made a purchased, registered for a new program, downloaded a file or attachment, or signed up for a contest via the email. Both of these rates can show marketers how effective their content is in creating customers or maintaining relationships.
Marketers should also examine their subscriber lists to gauge how effective their email marketing campaign is. Determining whether a list is generally growing or shrinking can indicate strong or weak email marketing. Campaigns that host high unsubscribe rates are obviously not doing email marketing the right way.
Websites are another platform that can use data metrics to evaluate marketing success. Like email automation, certain metrics (e.g. bounce rates and conversion rates) can be useful in measuring a website’s success. However, websites also can consider other metrics as indicators for success.
Shopping cart abandonment rate is a website-specific metrics. Ecommerce companies should be aware of the amount of abandoned shopping carts compared to actual purchases. Knowing this can help a company make necessary adjustments to keep abandonment rates low. For example, an ecommerce company might use its abandonment rate to determine whether it should implement an automated abandoned shopping cart email into its marketing strategy.
Marketers should also look at their site’s churn. Churn measures the amount of customers that come back or leave the company each month. Companies with a high churn rate may want to reexamine their marketing strategy, and make necessary improvements to their marketing strategy.
Regarding both email and website automation, marketers should evaluate their ROI rate. ROI (return on investment) is another key way to measure the success of marketing. This measurement shows the company’s net profit compared to the company’s investment.
As marketing digitally continues to change, marketers should keep a steady eye on their ROI rates. A dramatic increase or decrease in ROI signals that marketers are doing something right or wrong.
While these are a lot of numbers and metrics to consider, taking the time to evaluate each data set can be a huge marketing asset. If number crunching isn’t your strong suit? Learning more about data management platforms is a must!
A recent survey of online shoppers shows that consumers are more open to marketing personalization strategies than ever before. 79% of U.S. consumers expect personalization from brands, and over 50% expect e-commerce sites to remember past purchases. With these expectations and demands from the public, why hasn’t every company utilizing personalized marketing?
We’ve found that unwillingness to incorporate personalization comes down to one factor: Fear.
Fear of change
As marketing personalization is a relatively new concept, companies that are doing well without utilizing this marketing strategy may not want to make changes.
An article by Forbes explains why companies hesitate when making big changes.
Old habits act as a resistant towards change. These habits can be powerful and hard to break, pulling us away from new, alternative ideas. The brain is also responsible for resistance to change. One’s prefrontal cortex must work harder when experimenting with new ideas. With this in mind, companies may be reluctant toward embracing the new concept of personalization.
However, while embracing change may be difficult, doing so is imperative for a company that hopes to stay successful. Those who fail to utilize new approaches risk becoming outdated by competitors who conform to the fresh marketing tactics the public wants.
Fear of turning off customers
Personalized marketing is designed to give customers a 1:1 experience with products that are relevant and tailor-made for each individual. So why would some customers be turned off by this approach?
The creepiness factor.
Consider this blunder Target made a couple years ago. The company sent coupons for baby items to a teenage girl. Using the girl’s Guest ID number, name and historical buying data, Target had determined the girl was pregnant…however, the baby item coupons arrived in the mail before the girl had told her father. As chaos ensued, Target was met with skepticism. Critics called the company’s personalization practices “creepy” and “eerie.”
Situations like this give personalized marketing bad stigma. However, if the right precautions are taken, personalized marketing will not turn away customers.
Consider this: When a consumer is aware that their information is being tracked, they may be more comfortable sharing their data. Target had come across as invasive because neither the girl nor her family was aware that Target had been tracking her purchases and personal information.
Contrast this situation with a strategy employed by Nordstrom. Nordstrom uses marketing personalization by showing products similar to ones a customer has previously viewed. Because Nordstrom is so open about their use of personalization and data tracking, people are not fazed by it.
Nordstrom’s take on marketing personalization
Rewards in exchange for information can help customers feel at ease with the creepiness factor. Reward customers for sharing their email information with special offers each month exclusively through email. Similarly, asking a customer for his or her date of birth can go from creepy to personal with the promise of a birthday card and special gift delivered during their special month.
While personalized marketing can admittedly come across as creepy, companies aware of the boundary between personal and invasive should not fear the concept of a personalized marketing campaign.
Fear of the payoff
If your company is spending money on personalizing an experience, will it pay off in the future? Or is your company wasting time, money and other resources on something that could end up costing more than its worth?
How can you be sure you’ll reach your desired return on investment?
Based on these statistics, we are confident that personalized marketing will generate positive results for companies. Personalization makes customers feel special, generates a higher response rate, strengthens loyalty, and increases customer lifetime value. Personalized emails in particular are able to generate 18 times more revenue, compared to generalized emails. 78% of CMOs believe that custom content is the future of marketing.
With so many positive effects from personalization, it’s difficult to imagine an unsatisfactory ROI after implementation. These stats should qualm any uneasiness about payoffs a company may have.
Key Takeaways:
While changing adding a personalization aspect to marketing campaigns may seem like a big leap of faith, doing so is more of a help than hinderance to companies. However, if used correctly, personalization gives your company the edge it needs to stay competitive, retain customers, and increase ROI. Need a few tips for getting started with personalizing your marketing strategies? We have determined four steps to implementing marketing personalization.
Astronomy, like many other sciences, is heavily reliant on data analysis. Scientists looking at stars and planets record a multitude of variables including heat signatures, brightness levels, radiation, and even high level chemistry equations. As a result, it’s not too difficult to see how scientists make discoveries much later than the data shows.
In 2009, the space observatory, Kepler, named for early astronomer Johannes Kepler, was launched with the mission of discovering more exoplanets. Exoplanets are Earth-like planets that orbit stars outside of our solar system. Kepler was extraordinary at its job, almost too good in fact. Using a photometer that monitors the brightness of almost 150,000 stars, as of February of 2014 Kepler has discovered over 900 confirmed planets as well as another 3600 unconfirmed.
Discovering 900 planets out of over 4000 candidates is no small feat, but on February 26, 2014 scientists discovered an astounding 715 new planets from the Kepler’s old existing data via big data analysis. Using a tool called ‘verification by multiplicity’ NASA scientists were able to comb through overwhelming amounts of data with pinpoint accuracy. This sophisticated big data technique has roots partly based in probability and can be used for “wholesale validation” according to Jason Rowe, a member of SETI (Search for Extraterrestrial Intelligence).
Thanks to big data analysis, a groundbreaking astronomical discovery has been made. The next question though is “How do we apply this to the business world?” A giant space observatory returning an overwhelming amount of data is not that far off from a company with millions of customers. Using this same principles one could glean industry insights that are invaluable to any company’s success.
Delivering a hyper-personalized email marketing campaign isn’t the only avenue to delivering relevant content and offers to each and every customer. Website personalization gives customers access to a personalized onsite experience.
What’s the difference between a personalized and non-personalized website?
Non-Personalized – The content presented is the same for all customers and lacks personalization. Finding desired products and content relies on search and navigation. The customer must first select an item before it’s presented to them.
Personalized – The content presented is controlled, targeted and based on your big data. When a customer clicks through, targeted products and offers will be displayed based on their browsing history, purchases made, website behavior, lifestyle, etc.
As a marketer you want every customer to experience something personal and relevant. In doing so, you need to act on big data insights to segment and deliver personalized experiences in real time. This, in turn, drives higher conversions and revenue.
How can you turn non-personalized into personalized?
Personalized Greeting – When a customer clicks through, have a personalized greeting such as, “Welcome back, Mrs. Jones.”
Personalized Options – This includes the option to view account information, edit and update profile, add a profile picture and once click features that pulls in targeted information.
Visible Browsing History – Let’s say Mrs. Jones was shopping for workout apparel and had viewed a dozen items. Before making a purchase she was interrupted and had to step away for a few hours. When she returns and clicks through, a visible browsing history would pull in the items she’s previously viewed. This prevents Mrs. Jones from having to re-search the items.
Personalized Content – When a customer clicks through, it’s important to have a hyper-personalized landing page based on preferences, browsing & purchase history, profile etc. Mrs. Jones has browsed and purchased workout apparel and home office solutions. The landing page, or a portion of the content, should address her needs and preferences.
Smartphone apps typically only collect data during a workout. However, technological advances are helping fitness fanatics track their health 24/7.
