Tag Archives: big data

Marketing Automation is enabled by Artificial Intelligence, Big Data and Chatbots

“Marketing automation is growing – sizzling fast, announced Michael Jans recently in his blog AgencyRevolution.com. In fact, there are eleven times more B-B companies using marketing automation than were in 2011 (VBInsight). Most visible marketing automation for retail customers are chatbots.

Advancements in artificial intelligence (AI), coupled with the proliferation of messaging apps, are fuelling the development of chatbots.  Artificially intelligent chatbots or conversational agents can be used to automate the interaction between a company and customer.

What is Marketing Automation?

Marketing automation, in general, complements interactive and direct marketing with the help of automation and further on in CRM and email marketing 4. The goal of marketing automation is to target the right customer with the right content 1. To achieve this goal, the optimization of customer data – e.g. name, contact information, transactional data is critical. Consequently customers can be targeted with the right message. Therefore marketing automation allows marketers to respond instantly to identified opportunities in real-time even outside the marketing plan.

This use of marketing intelligence provides valuable management insights to markets, customers and campaigns and leads to enhanced efficiency. Also, this same use of data enables customers to receive personalized, relevant messages and offers at appropriate times. As result of this, customer experience is improved significantly. Indeed, Sarah Burke of Spokal concurs: “Marketing automation is a super effective tool when it’s used to supplement our marketing efforts in an attempt to make the lives of our customers even better”.

However, marketing automation is facilitated by Artificial Intelligence.

Artificial Intelligence

Techopedia defines Artificial intelligence (AI) as an area of computer science that emphasizes the creation of intelligent machines that work and reacts like humans. AI is becoming part of our lives ever more. Today we can ask a computer questions, sit back while semi-autonomous cars negotiate traffic, and use smartphones to translate speech or printed text across most languages. For AI to work properly, the machines or robots needed to be ‘learned’.

Machine learning is the process that offers the data necessary for a machine to learn and adapt when exposed to new data. Nello Cristianini suggests we should think of it as training a machine: “It depends on the other two methods by reading mined data, creating a new algorithm through AI, and then updating current algorithms accordingly to “learn” a new task.”

For most retailers and marketers in the digital economy, the intelligent ‘machines’ of choice are chatbots. However, chatbots are dependent on a host of interconnected and emerging technologies, many of which rely on machine learning and require massive amounts of data 3.

The use of data to enable Marketing Automation

Douw G Steyn, owner of the Bricks2Clicks (this blog) had this to say about Big Data: “One of the fall outs of the digitization of business is the massive amount of data that are everywhere. Every time a customer makes a purchase online or registers online, data is generated. The data can potentially tell you almost everything about consumers.”

Randy Bean in MITSloan commented on the use of Big Data with AI: “The impact of Big Data goes well beyond simple data and analytics. Big Data and AI in combination are providing a powerful foundation for a rapidly descending wave of heightened innovation and business disruption. While the first wave of Big Data was about speed and flexibility, it appears that the next wave of big data will be all about leveraging the power of AI and machine learning to deliver business value at scale.“

Data mining can find the answers to questions that you hadn’t thought to ask yet. What are the patterns? Which statistics are the most surprising? What is the correlation between A and B? (upfrontanalytics.com).

“Intelligent machines need to collect data – often personal data – in order to work. This simple fact potentially turns them into surveillance devices: they know our location, our browsing history and our social networks. Can we decide who has access, what use can be made of the data, or whether the data gets deleted for ever? If the answer is no, then we don’t have control” says Nello Cristianini in the New Scientist.

Chatbots as interactive conversational platforms

By definition, a chatbot is a computer program that responds to natural language text and/or to voice inputs in a human like manner 2. Chatbots can run on local computers and phones, though most of the time they are accessed through the internet (Chatbots.org). Moreover, the effectiveness of Chatbots is depended on the quality of the source data and how well they are programmed. They are after all robots! And robots need to be learned…

Once a customer starts to interact with a chatbot, the chatbot’s software identifies the customer. The chatbot will then have the demographic information of the customer, her purchasing history – such as what products she’d purchased most frequently, what time of the year she does most of her shopping, and when last did she purchased? The scope and depth of information can be never-ending.

The  of a typical conversation between a chatbot and a retail client (image: Chatbotsnewsdaily)

Eric Samson writing in Entrepreneur.com mentioned 7 benefits using chatbots as marketing tools

  1. Customer service – by providing the chatbot option for customers, you will lower the stress of dealing with customer service and increase customer satisfaction with your brand.
  2. Consumer analysis – chatbots can play a large role analysing customer data, and optimizing sales and marketing strategies in light of this analysis.
  3. Personalized ads – another chatbot strategy that’s proven to be successful is the creation of personalized ads.
  4. Proactive customer interaction – chatbots are ideal for “reach out” initiatives. To do this, the accompanying action should be something small, like inquiring whether or not the customer needs assistance.
  5. Site feedback – chatbots are great for reaching out to customers via simple questions and the gathering of feedback. This strategy is useful, especially for website optimization.
  6. Lead-nurturing – using the information that chatbots collect about a customer, you can create customized messaging that guides the consumer along his or her “buyer’s journey,” ensuring movement in the right direction that achieves higher conversion rates.
  7. Maintain a presence on a messenger act via a chatbot – by maintaining a presence on a messenger app via a chatbot, you can save money while simultaneously remaining available for your customers 24 hours a day.

According to Chatbot Conference, the 3 main disadvantages of chatbots are:

  1. Too many functions – most of developers strive to create a universal chatbot that will become a fully-fledged assistant to user. But in practice functional bots turn out not to cope with the majority of queries.
  2. Primitive algorithms – AI chatbots are now considered the best as they can respond depending on the situation and context. However, complex algorithms is required for this purpose. Meanwhile, only IT giants and few developers possess such powerful technological base.
  3. Complex interface – talking to a bot implies talking in a chat, meaning that a user will have to write a lot. And in case a bot cannot understand the user’s request, he will have to write even more. It takes time to find out which commands a bot can respond to correctly, and which questions are better to avoid. Thus, talking to a chatbot does not save time in the majority of cases.

Concluding

With Big Data, Artificial Intelligence and Chatbots there aren’t a clear ‘pecking order’. The Upfront Analytics Team explain it as such: “Data mining, artificial intelligence, and machine learning are so intertwined that it’s difficult to establish a ranking or hierarchy between the three. Instead, they’re involved in symbiotic relationships by which a combination of methods can be used to produce more accurate results.”

The speed at which technology is moving forward – “software is developing software” and “machines are building machines” an affordable, practical usable chatbot for customer care and marketing is not far away…

Read also:

  1. Predictive Analytics helps Retailers to make sense of Big Data
  2. Chatbots in Retailing – a Fact or a Fad?

Notes

1 Mattila, J. 2016. Customer experience management in digital channels with marketing automation, Master Thesis, University of Oulu, Department of Information Processing Science.

2 D’Haro, L.F. and Lue, L. 2016. An Online Platform for Crowd-sourcing Data from Interactions with Chatbots. Proceedings of WOCHAT, IVA.

3 Etlinger, S. 2017. The conversational business: How chatbots will reshape digital experiences, Altimeter.

4 Sandell, N. 2016. Marketing automation supporting sales, Master’s Thesis, University of Jyväskylä.

Image

Pixabay

 

Implementing Social Customer Relationship Management in Retail

One of the most important goals for retailers is to maintain long-term and profitable relationships with their customers. The construct Customer Relationship Management (CRM) started when retailers moved the orientation of their business from their companies to their customers. However, the advent of the internet, Web 2.0, and online social networks have disrupted the traditional way that retailers communicated with their customers.  Hence, Social Customer Relationship Management (SCRM) came to the fore because of the emergence of a “social customer”.

Social customers comprise the 2.8 billion* active social media users (Dr Dave Chaffey *, Smart Insights, 27 Apr, 2017). With these billions of social media users, retailers are no longer in control of customer relationships. Instead, customers and their highly influential virtual networks are now driving the conversation, which can trump a retailer’s marketing, sales and service efforts with their unprecedented immediacy and reach 1. However, social media needn’t to be a threat for retailers. Indeed, retailers that learn how to use social media technology to their advantage can gain valuable insights about the demographics and buying behaviour of their customers.

The use of technology for successful Social Customer Relationship Management

Social networks offer retailers practicing Social Customer Relationship Management masses of customers who group themselves around a brand 2. It is here, in these networks, that retailers can study the community’s behavior toward a brand or firm beyond purchase. The data originate from motivational drivers such as word-of-mouth activity, recommendations, customer-to-customer interactions, blogging, and the writing of reviews 3.

But retailers haven’t yet realized the opportunities of using their own data resources for Social Customer Relationship Management. Sandra Gittlen, mentioned  the following recently in CIO: “In an age where most companies have a social media presence on platforms such as Facebook, Twitter, LinkedIn, Snapchat and Instagram, it’s somewhat surprising that many still haven’t figured out how to turn the data gathered from company-owned properties and broader social media listening tools into automated and actionable intelligence”.

Trainor, Andzulis, Rapp and Agnihotri, (2014) 4 identified four functional blocks enabled by social media technology that are particularly relevant in a CRM context:

  1. Sharing – refers to technologies that support how users exchange, distribute, and receive digital content (e.g., coupons, texts, videos, images, “pins” on Pinterest, etc.). This is similar to the concept of information reciprocity – the activities and processes that encourage customers to interact and share information – which has been shown to positively influence a firm’s ability to manage relationships.
  2. Conversations – represents technologies that facilitate a firm’s interactive dialog with and between customers (e.g., blogs, status updates on Facebook and Twitter, discussion forums, etc.) and capture the information from these dialog.
  3. Relationships – represents the set of technologies that enables customers (and businesses) to build networks of associations with other users (e.g. Facebook, LinkedIn, Ning, Yammer, etc.) and allows organizations to utilize this network information.
  4. Groups – represents the set of technologies that support the development of online user communities centered on specific topics, brands, or products. Examples include SalesForce.com’s Ideaforce and Igloo’s Customer Community application software.

Integrating your Social Customer Relationship Management program with your marketing automation

SCRM deals with the strategies, processes and technologies that retailers can use to link the social web with their CRM strategy. According to Reinhold and Alt, (2012) 5, SCRM poses a challenge for large firms with numerous employees, market offerings and offices. Consequently, they need to discover the relevant conversation threads, synchronize information flows, initiate the appropriate actions and communicate at an individual level within millions of social web conversations.

However integrating SCRM with marketing automation is not impossible – you only need to start right. Malinda Wilkinson (DestinationCRM.com) advises that it’s important that your technology should always follow your process, not precede it. “Without this integration, it is difficult to create a consistent experience for your prospects and customers. And on top of that, too much time and too many resources will be drained trying to coordinate activities to ensure leads don’t fall through the cracks”, concludes Malinda.

Fitting your Social Customer Relationship Management program with your business philosophy

The success of an effective CRM system depends on the background marketing methods and business philosophy 2 of retailers. Therefore customer centricity should become the new strategic goal, where retailers build their brand and image together with their customers.

Linda Shea in AdAge.com proposes the following to become and remain a customer centric company:

  • Executives need direct interaction with customers. The key to executive buy-in, commitment and active support is first-hand knowledge and understanding of what is delivered to the customer, relative to their needs and desires.
  • All employees need to embody the intended customer experience. A narrative must be cascaded down to every single individual in the organization. Your employees must clearly understand their role in delivering the promise the narrative makes to the end customer.
  • Just say “no” to off-strategy ideas. Excitement abounds in most organizations with ideas and fresh thinking that may lead to new revenue streams. However, it is imperative to recognize that customer-centricity is not a destination but rather a multi-faceted, multi-year journey that will require laser-sharp focus, commitment and investment.

Concluding

Retailers that are not with their customers on the social networks will soon run out of customers. The Social Customer Relationship Management construct is customer centric by definition, giving retailers the opportunity, with the aid of marketing automation, to be part of the social media cloud.

Further reading:

  1. Finding Customers in the Vastness of the Internet
  2. Predictive Analytics helps Retailers to make sense of Big Data
  3. Demise of Loyal Retail Customers in the Digital Age

Notes:

1 Heller Baird, C. and Parasnis, G. 2011. From social media to social customer relationship management, Strategy & Leadership, 39(5):30-37.

2 Bagó, P. and Voros, P. 2011. Social customer relationship management, Global Journal of Enterprise Information System, 3(3):35-46.

3 Yoon, K. and Sims, J.D. 2014. Integrating Social Media and Traditional CRM: Toward a Conceptual Framework for Social CRM Practices, Harnessing the Power of Social Media and Web Analytics, IGI Global, Chapter 5:103-131.

4 Trainor, K.J., Andzulis, J.M., Rapp, A. and Agnihotri, R. 2014. Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM, Journal of Business Research, 67(6):1201-1208.

5 Reinhold, O. and Alt, R. 2012. Social Customer Relationship Management: State of the Art and Learnings from Current Projects. In Bled eConference, 155-169.

Image:

Flickr.com

 

Predictive Analytics helps Retailers to make sense of Big Data

“The most successful retail companies are utilizing data science and predictive analytics (PA) to improve efficiency, improve marketing campaigns, and gain significant customer insight for a competitive advantage” says Christine Kern, contributing for Innovative Retail Technology. But what about the “not so successful” retailers? How can they share in the advantages that Big Data and PA offer? Retailers can – by using predictive analytics.

What is Predictive Analytics?

Predictive analytics is a set of business intelligence technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behaviour and events, according to Eckerson (2007) 1. Or, as Eckerson states it more bluntly “Predictive Analytics is like an “intelligent” robot that rummages through all your data until it finds something interesting to show you.”

Also, forecasting is about predicting the future, and predictive analytics adds questions regarding what would have happened in the past, given different conditions. Therefore, PA attempts to quickly and inexpensively approximate relationships between variables while still using deductive mathematical methods to draw conclusions 2.

Gregg Brunnick, Director of Product Management & Technical Services, Business Systems Division, Epson America explains the usefulness of PA: “If you know how many cheeseburgers John sold during last Tuesday’s lunch hour, for instance, you can improve the efficiency of your food ordering, preparation, labor, and marketing operations.”

The value of Predictive Analytics for retailers

Deon Abott of Smarter HQ writing in Inside Big Data, suggests that data science and predictive modeling have become the holy grail for the retail industry. For this reason retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions.

These measurements provided general insight into the behavioral tendencies of customers. However, says Deon “In order for retailers to create a meaningful dialogue with customers that honors the shopper’s preferred level and mode of engagement, it takes more than summarized reports, which is why customer intelligence and predictive analytics provide the opportunity to significantly change the retail marketing industry.”

Generic uses of Predictive Analytics are according SAS the following:

  • Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cyber-security becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud.
  • Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
  • Improving operations. Many companies use predictive models to forecast inventory and manage resources. Predictive analytics enables organizations to function more efficiently.
  • Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness.

Erick Siegel of Big Think suggests that predictive analytics allows for a keen assessment of the probability that any one person will buy, sell, click, lie, die, etc. PA doesn’t just predict the future; it can influence it as well.

The challenges of using Predictive Analytics

The big challenge for retailers is to use PA correctly. Not using PA appropriately can cause loss of brand equity and market share with astonishing speed. The key is in understanding the customer’s “digital body language”, suggests Earley (2014) 3. Retailers need to understand customer data – the attributes, needs, characteristics, life stage, behaviour, demographics, and psycho-graphics. The information coming from the data may be used to help customers behave in a way that satisfies their needs 3.

Unfortunately, the use of PA by some retailers has been reported as controversial. Not only are most companies not informing their customers of when and what data they are collecting, but they are not letting them know about their analysis policies, according to Corrigan et al (2014) 4.

According to Arliss Coates from EConsultancy retailers should note the following when using PA:

  • Is automation driving out your innovation and originality?
  • Do you have people that know how to interpret the results of PA?
  • Scenario planning – humans cannot prepare the machines to anticipate every possible nuance or scenario.
  • An over-reliance on data to substantiate decision-making may hampers innovation.
  • The “garbage in, garbage out” principle – bad data will render bad results.

Concluding

The explosion of data is here to stay. At this moment it seems that the availability and use of big data and predictive analytics will grow exponentially. In spite of some controversy and challenges, PA couldn’t have come at a better time for retailers. Predictive analytics may help retailers to integrate their channels more smoothly and thereby keeping in pace with their competitors.

Read also: Big Data for Small Retailers – Is it Doable?

Have a look at this practical demonstration of PA from IBM “”Predictive Analytics for Retail – Introduction”:


Notes

1 Eckerson, W.W. 2007. Predictive Analytics. Extending the Value of Your Data Warehousing Investment, TDWI Best Practices Report, Q1.

2 Waller, M.A. and Fawcett, S.E. 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, Journal of Business Logistics, 34(2):77-84.

3 Earley, S. 2014. Big Data and Predictive Analytics: What’s New? IT Professional, 16(1):13-15.

4 Corrigan, H.B., Craciun, G. and Powell, A.M. 2014. How does target know so much about its customers? Utilizing customer analytics to make marketing decisions, Marketing Education Review, 24(2):159-166.

Image

Pixabay

Video

IBM

Big Data for Small Retailers – Is it Doable?

Do Big Data (BD) for small retailers offer an opportunity to compete with the big retailers or is it too much trouble? One of the fall outs of the digitization of business is the massive amount of data that are everywhere. Every time a customer makes a purchase online or registers online, data is generated. The data can potentially tell you almost everything about consumers. Retailers that sort, analyse and interpret BD can add value for customers and so increase their shopping experience.

Surely retailers should take advantage of BD since it contains captured detailed information that probably was overlooked in the past. However, to get the most out of BD, retailers need to be innovative. The promise of new revenues, customers, and new businesses with BD will require development and investment in teams and technology 1. But first let’s have a look at what BD is all about…

What is big data?

Big data is a term that primarily describes data sets that are so large, unstructured, and complex that it requires advanced and unique technologies to store, manage, analyse, and visualize 2. Therefore, big data represents the data sets that cannot be perceived, acquired, managed, and processed by traditional IT and software/hardware tools within a tolerable time 3. Compared with traditional data sets (small data), big data typically includes masses of unstructured data that need more real-time analysis, according to Chen, Mao, and Liu, (2014).

Where can retailers find Big Data? Rajdeep Nair responds as follows on Quora: “Data is everywhere… it can be purchase data or images uploaded by you on the social media site or data sent by mission sent to Mars by NASA. Everything that is there on the internet and company or an organisation’s confidential data stored on the server. Mostly  data is stored on the server, the technology of which is improving and evolving rapidly.”

However, a good place for small retailers to find “Big Data” is on their own systems. Have you ever analysed your own data sets before?

What retailers can do with Big Data

According to Russell Walker 1, firms that are first movers in leveraging BD have great advantages because they develop innovative insights about customers and markets. These insights can transform services, and even business models. Bernard Marr, contributing to Forbes declared Big Data as “A game changer in the retail sector”.

Bernard notes that Big Data analytics is now being applied at every stage of the retail process. Says Bernard: “BD is used to understand what the popular products will be by predicting trends, forecasting where the demand will be for those products, and optimizing pricing for a competitive edge.”  Moreover helps BD retailers to identify the customers that are likely to be interested in their products and works out the best way to approach them. It also to help them making the sale and working out what next to sell them.

Alex Woodie writing a piece in Datanami.com suggests there are 9 ways retailers are using big data technology to create an advantage in the retail sector.

The advantages of Big Data to retailers

  1. Recommendation Engines – by training machine learning models on historical data, the savvy retailer can generate accurate recommendations before the customer leaves the Web page.
  2. Customer 360 – customers expect companies to anticipate their needs, to have the products they want on-hand. Also to communicate with them in real time (via social media), and to adapt to their needs as they change. In the cutthroat world of retail, developing a customer 360 system using Big Data may be a matter of survival.
  3. Market Basket Analysis – is a standard technique used by merchandisers to figure out which groups, or baskets, or products customers are more likely to purchase together. It’s a well-understood business processes, but now it’s being automated with the help of BD.
  4. Path to Purchase – analyzing how a customer came to make a purchase, or the path to purchase, is another way big data technology is making a mark in retail.
  5. Social Listening for Trend Forecasting – platforms like Hadoop were designed to facilitate the handling and analysis of large amounts of unstructured data, such as Facebook posts.
  6. Price Optimization – setting the right price requires knowing what your competitors are charging. Data can be collected electronically using daemons that crawl competitors’ website to get detailed info about product pricing.
  7. Workforce and Energy Optimization – big data technology can deliver benefits on the marketing and merchandising side. As a result it can help big retailers optimize their spending on human capital.
  8. Inventory Optimization – by analysing BD, retailers can plan their seasonality in the shipping algorithms better.
  9. Fraud Detection – retail fraud is a huge problem, accounting for hundreds of billions of lost dollars every year. Retailers have tried every trick in the book to stop fraud, and now they’re turning to big data technology to give them an edge.

Concluding

The narrative about Big Data is more with ‘Big Retailers’ at this moment. However, with smaller retailers adding the online channel to their business, there are ample opportunities for them to use their own data to great effect. Everything else will cost retailers a lot of money. Maybe to start with small data is better for smaller retailers.

Have a look at this video by Tera data corporation more more on Big Data for retailers:

Notes

1 Walker, R., 2015. From big data to big profits: Success with data and analytics, Oxford University Press.

2 Xu, Z., Frankwick, G.L. and Ramirez, E. 2016. Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research69(5):1562-1566.

3 Chen, M., Mao, S. and Liu, Y. 2014. Big data: A survey, Mobile Networks and Applications, 19(2):171-209.

Image and video

Pixabay

Tera Data Corporation

Personalization of Marketing Communication – not just for your Customer’s sake

Personalization of marketing communication is not just a good practice for retailers, but also a way to help their businesses survive. The advent of the internet has rendered retailers the opportunity to offer their customers products specifically customized for them. This is in direct contrast with mass marketing where the objective is to broadcast product offerings to reach the largest number of people possible.

Personalization of marketing communications is to treat each person as a unique individual with distinctive needs and to provide them with customized solutions 1. To be able to personalize marketing communications, retailers need to learn about the customer’s individual needs and preferences in terms of the types of content that the customer is willing to receive and other person-specific characteristics.

The strategic use of data collected during the online buying process and social media sites may be a good starting point for retailers to know their customers better.

Data – the foundation for the personalization of marketing communication

The digitalization of the entire advertising industry is generating ever increasing amounts of data that must be collected, analysed and interpreted 2.  Lying hidden in all this data is information, potentially useful information that is rarely made explicit or taken advantage of. We must just find the data.

The data we need is right before our eyes. Says Woopra: “Social media interactions, email marketing, landing pages, surveys, customer relationship management (CRM) tools, and re-targeted ads are all customer touch points that can tell you about your customer’s needs and interests”.

Once the data are sorted and tabled, retailers can segment and target their customers and also position their products accordingly. However, here the process is done for each customer specifically according the individual’s unique needs, desires and behaviors (customization). So, once customization has been achieved, it makes personalization of marketing communication possible.

Personalized marketing communications used by online retailers

Online shopping has become an important channel for retailers. Unfortunately, it does not afford facile development of an interpersonal relationship or facilitate easy interactions between buyers and sellers 3.  Even worse, many retailers use the online channel to send generic marketing messages via email or text, to the annoyance of their customers. This, however, is not personalized marketing communication.

Retailers need to collect and analyse data about the buying behaviour of individual customers. The profile of the customer will provide guidelines for the retailer how to personalize his/her marketing communication message. Daniel Newman, CEO of Broadsuite Media Group suggests the following ways brands can use data to build personalized marketing tactics:

  • Capture complete data – are you collecting every piece of data that you possibly can? Brands today have more consumer information at their fingertips than ever before, and they can use that data to get to know their customers in depth.
  • Social data – social cues and signals are excellent ways to figure out more about customers than traditional sources like email, demographics, or purchase records.
  • Segmentation – you need to segment your audience into smaller groups for more accurate targeting.

What does a personalized marketing message looks like?

You’ve done all the hard work by sourcing and sorting your customer data. Now it is time to create a personalized marketing message for your customer. Below is an image from GIGYA, a customer identity management agency. The ad shows beauty products that are specifically recommended for a customer with a unique skin type and facial features.

Note that the narrative is in the second person – thus the ad is addressing the individual personally.

The advantages of personalized marketing communications

Retailers that personalize their marketing communication may enjoy the following advantages says Infor Marketing Management:

  1. Improved Return on Investment (ROI) – one study found that personalized website experiences resulted in an average 19% increase in sales. For email, personalization is even more powerful, generating transaction rates and revenue six times higher per email than non-personalized emails.
  2. Outflanking the competition – with personalization, retailers can increase the impact of each interaction to get consumers’ attention and time online – at the cost of the competitors.
  3. Customers expect it – most consumers said it’s important to receive relevant offers when shopping online. And, almost a third wants more personalization during their online shopping experiences, reports Infor Marketing Management.

Concluding

“Personalization is retail’s future; especially as more advanced technologies allow marketers to handle personalization more effectively”, suggests Infor Marketing Management. However, retailers have to invest in the right technology, including marketing automation, CRM, social media management and data analytics tools, as well as more advanced e-commerce platforms.

Bringing the person back into the marketing message may help soften the total onslaught of marketing atomization by means of the internet of things, big data and bots.

Have a peek at this short video from Evergage re personalized marketing communication.

Notes

1 Järvinen, J. and Karjaluoto, H. 2015. The use of Web analytics for digital marketing performance measurement, Industrial Marketing Management, 50:117-127.

2 Grether, M. 2016. Using Big Data for Online Advertising Without Wastage: Wishful Dream, Nightmare or Reality? GfK Marketing Intelligence Review, 8(2):38-43.

3 Lee, Y.J., and Dubinsky, A.J. 2017. Consumers’ desire to interact with a salesperson during e-shopping: development of a scale, International Journal of Retail & Distribution Management, 45(1):20-39.

Read also:

  1.  Chatbots in Retailing – a Fact or a Fad?
  2.  Retail and the Internet of Things

Images and video

  1. Pixabay
  2. GIGYA,
  3. Evergage