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Personalized Marketing Best Practices

Three best practices in data-driven personalized marketing include data onboarding for 1:1 ad marketing, using artificial intelligence, and using streaming analytics.


  • Also known as offline data, data onboarding include in-store purchases, loyalty card data, and could be obtained from CRM data files or bought from offline data vendors.
  • The data examined would allow marketers to target the right audience and deliver more personalized marketing campaigns and maximize ROI.
  • The value of data onboarding is expected to reach $1 billion by 2020.
  • Businesses could make better marketing decisions and increase their bottom-line if they adopt the location-centric approach.
  • Advertisements could be tailored based on the current location and eating, shopping, and socializing habits of consumers.
  • A shoe store can implement data onboarding by using its loyalty card to store data of its target customers on its offline POS systems.
  • An onboarding system will gather the data and match them with computer cookies of certain customers to send targeted ads to customers who had shown interest in that shoe.
  • The success of data onboarding is measured through increased conversions or an ROI measure.
  • GrubHub has used data onboarding to collect data about what, where, and when people eat and use the data to create engaging content such as quizzes, polls, and contests.
  • Comcast has used data onboarding platform LiveRamp to identify specific households they should target specific ads to drive costs per thousand impressions (CPM) down as much as 35% while increasing its reach.


  • Marketers could use AI-powered data analytics tools to better understand and reach their consumers, and adopt the best strategies to improve their businesses.
  • According to a Forrester study, 89% of marketers believed that predictive analytics would be an important part of their work in the future.
  • AI tools could be implemented to analyze social media data to determine their consumers’ likes and dislikes.
  • Marketers have used Facebook to target specific ads at people based on their interests.
  • AI-powered chatbots are being used to communicate and engage with customers, and also collect data to better understand them and make product recommendations.
  • The success of AI-power tools is measured through ROI and the amount of savings from automating repetitive tasks.
  • Amazon’s Alexa is capable of driving product recommendations to its consumers.
  • Around 35% of Amazon’s revenue is driven by its recommendation engine.


  • Marketers could leverage streaming services to obtain psychographic data of their consumers.
  • Streaming analytics focus on the present instead of historical data and represent a simpler and lower-risk approach that allows businesses to measure results.
  • Building a streaming analytics platform is considerably cheaper and less time-consuming than building database infrastructure.
  • Sports streaming services provide marketers with data such as consumers’ favorite players and teams.
  • Streaming analytics is implemented by gathering data for specific events from various sources such as online transactions, social media posts, web browsing, and wireless calls.
  • A dedicated app is typically used to interact with consumers to gather the required data.
  • The success of streaming analytics is measured through customer retention, loyalty, ROI, and ROAS.
  • 58% of enterprises that have adopted streaming analytics have seen a significant increase in customer retention and loyalty.
  • Digital agency PMG has used streaming analytics to help a client construct consumer profiles based on real-time transactions.
  • This has resulted in an increase in conversion rate during key holiday periods and a return on ad spend of $149.59 per customer.

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Future of Personalized Marketing

Five insights on the future of data-driven personalized marketing include the use of Data Management Platform (DMP), identifying deeper patterns in data for hyper-personalization, the use of deep learning to drive customer retention, big data analytics, and increase in the usage of augmented reality and virtual reality.


  • Personalization is a key part of many marketing strategies. However, it remains a very difficult task for marketers to pull off effectively.
  • In a recent survey of 190 marketing influencers conducted by Ascend2, 63% of respondents said that data-driven personalization is a difficult task to execute.
  • In a survey conducted in March 2018, from Evergage and Researchscape International of 300 marketers in the United States, just 6% of respondents gave their personalization efforts an A rating and 46% gave themselves a C.
  • According to a survey conducted in January 2018 by Verndale, 200 U.S. senior decision marketers stated that personalization is very important for increasing sales and improving customer satisfaction and retention. Yet, 84% of survey respondents agreed that the potential of personalization has not been fully realized.



  • By 2020, marketers expect their Data Management Platform (DMP) usage to rise by 64%.
  • More than 55% of marketing leaders currently use a Data Management Platform (DMP) and an additional 35% plan to adopt one within the next two years.
  • Although many marketers only see DMPs to be useful for solving basic issues like content personalization and frequency caps on ads, high-performers have begun to unlock an additional DMP feature — delivering audience insights.
  • For example, when a customer visits the website from their mobile device, DMP will recognize them in that moment and create a real time experience for them. In other words, it solves the issue of 'identify crisis' and figures out who they are.

2. Identifying deeper patterns in data for hyper-personalization.

  • Marketing automation with machine learning (ML) allows you to personalize customer experience based on their history of interactions, like purchasing habits, behavioral traits, and digital preferences.
  • Deep learning technology, on the other hand, will not just rely on the interaction history of the customer but will also consider their intent.
  • Deep learning is much better than other ML and AI techniques in understanding what customers want, as it has the potential to find patterns inside of patterns.
  • Deep learning techniques identify and analyze patterns so it can predict real-life outcomes. But, the technology is still in its infancy and what it’s capable of is yet to be seen.

3. Use of deep learning to drive customer retention

4. Big data analytics

  • Prescriptive analytics is another technique that uses deep learning from customer data to predict future trends and behavior patterns.
  • Marketing automation platforms have become powerful enough to anticipate predictions like when a customer will make his next purchase, who are the most valuable customer etc.
  • Deep learning has already been used in the advertising industry to make activities up to 50% more efficient. As a result, many marketers are intrigued by it. But, deep learning can still be difficult and marketers need to know how it works and how they can use it to their advantage.
  • Marketers who strive to be relevant to their consumers, need to pay attention to this technology.

5. Increase in the usage of augmented reality and virtual reality — shopping

  • Augmented reality (AR) and virtual reality (VR) are already being applied in gaming, cinema/entertainment, healthcare, military, space/flight simulations, and many more scenarios across almost every industry.
  • These technologies will continue to create disruption in marketing and retail.
  • Companies like IKEA, Wayfair, and Lowe’s are already using virtual reality to give shoppers a clearer and more realistic idea of how a product will look in their home (360° panoramic views, for example).
  • Wayfair enables customers to furnish and design their outdoor patio using VR technology. This is a sign that online and offline worlds will merge even more.
  • The use of AR, for example, will commonly allow customers to try on clothes virtually and see a 3D image of themselves wearing certain things or with their dimensions.
  • This can be done by using location data like beacons and geofences in-store and increased personalization like offering of clothing customers are likely to prefer, based on previous behavior.
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Personalized Marketing Customer Experience

Some trends in the Wellness industry/space for the use of personalized marketing to drive and improve customer experience include synchronization of data from customer devices to tailor personalized workouts, collection of account creation information for interaction with available services, payments and transaction information for delivery fulfillment among other trends.


  • Wellness devices such as the Fitbit user device collects data used to determine some metrics like the number of steps taken. Other items received from customers during device synchronization include distance traveled during workouts, calories burned, weight change, heart rate, sleep stages, active minutes as well as location.
  • The data transferred to the Fitbit server varies depending on the device a client is using.


  • The data collected from customer device like client height, weight, gender as well as age allows wellness companies to improve the accuracy of daily exercise. It also allows the company to determine the number of calories someone needs to burn, and the distance to be traveled to give a personalized experience.


  • During account creation, wellness companies such as Fitbit collect information such as customer name, gender, height, email address, password, date of birth, weight, and often a mobile telephone number. Customers often choose to provide additional information which may include a profile photo, biography, country information as well as community username.[17]


  • Wellness companies such as Fitbit utilize data collected during account creation for interaction with available services. This information is used to customize individual view or search contents, customize an installation, application or software, etc., to boost customer experience.


  • Some wellness companies such as Fitbit collect payments and transactions pieces of information. Customers are required to provide specific data for identification and verification, which may include their name, credit, debit or card expiration date, some other card number, and CVV code. The CVV code is encrypted then sent to the client card network.
  • For customer convenience, Fitbit only stores the last four digits of a card number and the card issuer’s name as well as contact information.


  • Fitness companies such as Fitbit store delivery address to fulfill (deliver) an order and boost customer experience.


  • For fitness customers utilizing third-party coaching services, wellness companies like Fitbit may save employer details, insurance company details, or any details of third-party coaching companies patronized.


  • Third-party details such as employers or insurance companies patronized are to determine customer eligibility for specific benefits, like discounts or free services aimed at boosting customer experience.



  • Detailed research reveals that Zenoti is utilizing artificial intelligence, predictive analytics as well as mobile solutions to successfully attract, engage and retain wellness customers.
  • Customers experience has been enhanced by the elimination of long front desk lines, to present omnichannel booking, contactless payment solutions and seamless check-outs through the AI-driven marketing automation designed exclusively for wellness centers like salons and spas.
  • Zenoti analytics assist wellness companies in gathering customer feedback, measuring it, and also acting on it. Zenoti analytics captures negative feedback so that negative customer experience is analyzed converted into an opportunity.
  • Orange Twist, a wellness company, utilizes Zenoti cloud-based solutions to enforce a consistent customer experience for its brand. Zenoti analytics gives Orange Twist real-time access to customer financial data, client preferences, and other insights valuable to multi-site businesses.
  • With advanced analytics information on customers, OrangeTwist prepares for and offers "differing product features" across its various locations in line with customer preference.
  • OrangeTwist provides personalized aesthetic care and takes time to listen to its client needs. Individual solutions offered to clients vary based on desired patient experiences.
  • Complete customer history, list of alerts as well as a smart booking system made available by Zenoti helps in personalizing experiences. Several dashboards provided by Zenoti to preview individual/customer details are accessible via this link.
  • Through the help of AI and predictive analysis, Zenoti sends out highly personalized and timely emails to customers thereby boosting customer experience. Zenoti's smart marketing technique is proven to raise salon and spa revenues between five and ten percent on a week over week basis.
  • Zenoti, a disruptive company offered solutions to thousands of salons and spars operating in the wellness sector in over 30 countries as of 2017. A 2019 publication reveals that Zenoti now has thousands of customers in about 44 countries.
  • A 2019 startup ranking web ranks Zenoti number two startup in Washington and one of the top 1000 in the world.


  • A RallyHealth web publication reveals that smartphones, wearable, and gamification have seized the wellness industry.
  • Gamification utilizes the data-driven methods used by game developers to engage customers and brings similar principles to marketing. The goal of gamification is to motivate specific actions and also add value to a business through customer engagement.
  • The healthcare gamification market is predicted to grow at a CAGR of 44% by the year 2019. Top players in the gamification market include Apple, FitBit, Nike, Google, Ayogo Health, Rally Health, among others.
  • According to insights obtained from a PricewaterhouseCoopers (PwC) 2016 survey, about 64% of corporate wellness programs now feature some competition or gamification.
  • Wellness companies use gamification to track specific health goals such as the number of steps taken or miles run and share the information on a public leaderboard on social media channels that rank individuals or teams. This goes a long way to boost customer experience.


To gain insights into trends in the Wellness industry/space for the use of personalized marketing to drive and improve customer experience, we researched through journals, surveys, white papers, among other resources for a list of trends that meet the above criteria. Unfortunately, there was no such publication about wellness industry trends unearthed. We also researched through credible databases such as American FactFinder, CrunchBase, among other databases for data on rankings, top trends or statistics indicating essential trends in the wellness industry. Unfortunately, we only uncovered a Startup Ranking web which ranked Zenoti an analytics service provider to companies in the wellness sector. Finally, we researched for granular details of disruptive trends practiced by individual wellness companies. We prioritized practices in the Wellness industry/space for the use of personalized marketing to drive and improve customer experience. We also prioritized companies like Fitbit, Zenoti among other highly rated wellness companies. According to CNBC, Fitbit was rated to be the number one free app to keep users healthy in 2018.


From Part 02
  • "The technology advancements in this field have empowered marketers to use customer data to unearth valuable insights and create highly personalized marketing communication, sent at the right time and in the right channel. No wonder it’s slated to become a $5.5 billion industry by 2019."
  • "More than 55% of marketing leaders currently use a Data Management Platform (DMP), and an additional 35% plan to adopt one within the next two years."
From Part 03