You are welcome to share this post.

Image by Rawpixel.com / shutterstock.com 


Introduction

Big Data is an exciting new development in computing that is revolutionising marketing and commerce in general. This article will define Big Data and outline how organisations are using it to become customer centric. It will outline a new ‘consumer purchasing decision journey’ as proposed by Court, Elzinga, Mulder & Vetvik (2009), discuss the 4 P’s Digital Marketing Mix (Lee, 2012) and critically assess how Big Data has transcended methods retailers use to market their products or services to their customers. This article will also examine how Trident Marketing used Big Data to transform their company, identifying untapped sources of profit, resulting in competitive advantage. It will also examine the pitfalls for any company considering using Big Data.

What is Big Data?

Ward and Barker (2013) define Big Data as “a term describing the storage and analysis of large and or complex data sets using a series of techniques.” McAfee and Brynjolfsson (2012) propose it is related to analytics, as both seek to capture intelligence from data and translate that into business advantage. Sicular (2013) of Gartner distinguishes Big Data from analytics using the three V’s:

1. Big Data consists of high Volumes of data with worldwide data doubling every 40 months. The data can be structured and unstructured, i.e. data that doesn’t easily fit into traditional databases such as images or machine data.

2. Importantly, the Velocity of data creation provides real-time or ‘fast’ data which allows companies to quickly identify trends and gain a competitive advantage over their competitors.

3. Finally, the Variety of data is growing with data originating from social networks, GPS signals, sensors and much more. The cascades of information flowing from these origins offer companies opportunities to gain insights about customers that are deeper and richer than anyone thought possible just a few years ago (Benady, 2013) .

Focus On The Customer

Drucker (1954) asserted that any business has only two basic functions: marketing and innovation. This is because he believes that the purpose of any business is to create a customer, and identifying the organisation’s best customers is a critical component to successfully growing a business. Big data affords marketers the opportunity to gain insights about customers that were never possible before. Organisations can collect data from many sources such as their own CRM system, the customers’ transactional data, social media, online platforms, market research, customer feedback and even weather forecasts. The challenge for marketers is to combine the data and analyse it to form one complete 360 degree picture of the customer (IBM, 2013). Doing this enables the marketer to gain insights into consumer demand, identify trends, monitor competitor activity and differentiate the company’s offering. More importantly, Big Data allows a marketer to tailor personalised marketing campaigns, such as coupon offers, and enhance the customer experience (Goller & Hoffman, 2013).

The customer’s data tells their story, their product preferences, their spending habits and affluence, their interests and past-times and much more. Knowing how to mine that data and translate that story into one of actionable and successful direct marketing campaigns is the science of statistical modelling: Descriptive Modelling (DM) and Predictive Modelling (PM). DM uses statistics to segment the customer base into distinct groups based on certain variables and this can uncover significant similarities and differences between the behaviour and demographics of each segment. Special attention is given to the best customers, which could be based on monetary spend, recency and or frequency of purchases, as this is the group that offers the greatest potential for increasing return on investment. PM always builds on the foundational findings associated with the DM and can be thought of as the statistical deployment of the characteristics defining the target group already identified in the DM. The most powerful aspect of PM is its ability to statistically identify and predict the customers with the highest propensity to buy through scoring by focusing on associations or relationships (Webster, 2013). The ultimate aim is for marketers to use the predictive model created to target these customers.

Big Data driven decision making removes the element of chance and increases the effectiveness of direct marketing campaigns thus increasing marketing return on investment (MROI). The marketer can better understand the company’s existing customers, craft creative messaging for customer acquisition, and connect with customers and potential prospects in ways that are meaningful and cost-effective for the organisation (Webster, 2013).

Customer Decision Journey – A ‘Pull’ Strategy
A goal of marketing has always been to reach customers at the point of influence rather than the point of purchase. Court et al. (2009) discuss a change in the purchasing funnel. In the past when making a purchase, a consumer would consider a number of brands and a company’s ‘pushed’ marketing activities, at various touch points, could influence the consumer’s decision to buy their brand over another. However, their research suggests a new customer decision journey, which proposes a much more active consumer evaluation of alternatives before deciding to purchase. In this digital age, online consumers are more connected, sharing brand experiences on social media, blogs and review sites. Research by McKinsey (2009) found that two thirds of consumers’ decisions to purchase are influenced by personnel recommendations. Traditional push marketing is still important but with this ‘shift of power from the publisher to the public’ (Grealish, 2012) marketers need to engage in ‘pull’ or consumer-driven marketing to enhance the customer experience which promotes brand loyalty, engagement and very importantly brand advocacy (Lee, 2012).

To enhance the customer experience Lee (2012) proposes the new 4 P’s Digital Marketing Mix; personalisation, persuasion, presence and permission. Online retailers, such as Tesco, Netflix and Amazon, use big data to segment and target customers by using real-time personalisation. They can track the behaviour of individual customers from internet click streams, update their preferences, and model their likely behaviour in real time. They will then be able to recognise when customers are nearing a purchase decision and gently ‘persuade’ the customer to purchase by bundling preferred products, offered with reward program benefits (McGuire, Manyika & Chui, 2012). Lambrecht & Tucker (2013) conducted research into companies using ‘Dynamic Retargeting’ or personalised ads based on customers’ data, specifically browsing habits. They found that the right personalised advertisement introduced at the right time of the consumer decision journey is a very effective technique to close a sale. The issue for the marketer is that it works well for their better customers but due to a lack of data on potential customers, it may not be as effective!
Brands also need to establish a ‘presence’ on social networks or platforms to engage with their customers but more importantly advances in technology in handling ‘Big Social Data’ allows companies to more easily integrate and analyse social data alongside their own business data (DataSift, 2013).

Proceed With Caution

With the permission of the customer, businesses can deliver highly personalised communications to each customer ubiquitously and at any time. While the marketing opportunity is huge, it’s more intrusive and personal than ever. As consumers continue to grow ever more powerful, the attention customers afford a brand is invaluable but companies must operate within the basic understanding that every piece of data collected requires implicit trust. Without customer permission or over communication, marketing messages will annoy customers and will be ineffective. The personalised messages must be relevant and feel like a service to the customer (Lee, 2012).

There are also other privacy concerns. Polonetsky and Tene (2013) discuss the concern of privacy advocates about the advances of the data ecosystem. They question whether the use of Big Data will shift the power of relationships between government, business, and individuals which could lead to invasive marketing by firms but also racial or other profiling, discrimination, over-criminalisation, and other restrictions of freedom. Of course everything is open to abuse but as long as marketers work within ethical guidelines, targeted campaigns and relevant information can be very beneficial to the consumer.

Achieving Success With Big Data

In 2007, Trident Marketing, an American direct response marketing and sales firm, with sales stagnated at $5 million changed their approach to data management. They introduced a new advanced database analytics system to help personalise campaigns and target customers, through both digital and traditional promotions. Their analytics model collects data internally from customer telephone calls, CRM and customer order systems with external data such as credit bureaus and real-time clickstream data from Google and Bing. The system consolidates thousands of data points regarding customers into one single analytics platform, enabling them to direct the right message to the right person at the right time.

With this powerful platform, they significantly increased their ROMI. The marketing department immediately could determine the optimal amount to bid per ‘click’ on the keywords that yield the highest volume and profit per sale. This insight alone decreased marketing costs by 30% and within 60 days increased sales by 10%. They also use predictive modelling to identify a customer’s propensity to churn and this information is incorporated into marketing costs to calculate the true cost of a campaign. Within 4 years they increased sales from $5m to $53m a year which is expected to rise to $100m in 3 years (IBM, 2013). This case clearly demonstrates that, if managed and implemented correctly, Big Data works!

Potential Pitfalls

As can be seen from the success of Trident Marketing, there is no denying that the possibilities offered by Big Data are phenomenal but companies must also be aware of the crescendo of hype around Big Data and the tech firms selling expensive data storage systems. Many brands have been guilty of wasting huge sums of money on creating ever-bigger databases that are never properly used (Kemp, 2013). Goller & Hoffman (2013) infer that companies using Big Data must first collect and manage as much relevant data as possible, then analyse and turn that Big Data into big insights. Finally, companies must act on that information to affect current business activities or initiatives. Piers North (2013 as cited by Kemp, 2013) UK Director of Yahoo summarises it by saying ‘Data is worth little; it is the insights and outputs that really matter.’

Research by MGI and McKinsey (2013) highlights another serious issue facing organisations: a shortage of skilled analysts and managers. Who will analyse, interpret and act on their data? The United States alone faces a shortage of 140k to 190k people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make business decisions based on this analysis. If this is the situation in the US then it is likely that Irish organisations seeking to employ qualified analysts and managers will find it extremely difficult.

Conclusion

Organisations using Big Data to enhance marketing are achieving competitive advantage through internal and external sources of data. Predictive modelling has removed the element of chance from marketing campaigns, increasing the performance of campaigns with personalised offers and a higher return on marketing investment. In this connected age, brand advocacy plays a major role in the customer decision journey and Big Data allows marketers to use real time personalisation to enhance the customer experience online, increasing brand engagement and brand loyalty. Some organisations are engaging in Big Data but they are not applying it correctly or using the information to gain valuable insights and a big challenge for organisations is in finding and recruiting data analysts and managers with the necessary expertise. There are also concerns around privacy but overall, most would agree that Big Data has the power to transcend marketing, giving firms a competitive advantage whilst delivering a better shopping experience to the consumer.

References

Benady, D., (2013) ‘The Big Data Breakdown’, Marketing Magazine, 13 Feb, available: UCD Ebsco Host Database [accessed 10 November 2013]

Court, D., Elzinga, D., Mulder, S., & Vetvik O. J. (2009) ‘The Consumer Decision Journey’ McKinsey Quarterly, June, http://www.mckinsey.com/insights/marketing_sales/the_consumer_decision_journey [accessed 11 November 2013]

DataSift (2013) ’New tools make social big data easier for enterprise’, 22 Aug, available: http://www.fiercebigdata.com/press-releases/datasift-launches-new-tools-make-social-big-data-easier-use-enterprise [accessed 08 November 2013]

Drucker, P., (1954) ‘The Practice of Management’ pp. 39–40, New York: HarperCollins.

Goller, B., & Hoffmann, S., (2013) ‘Leveraging Big Data for Precision In-Store Marketing – Turning Real-Time Data Into Big-Time Insights’, Retail Property Insights, Vol. 20, No. 1., available: UCD Ebsco Host database [accessed 08 October 2013]

Kemp, N., (2013) ‘Big Data moving beyond Hyperbole’, Marketing Magazine, Sept 2013, available: http://www.marketingmagazine.co.uk/article/1170378/big-data-moving-beyond-hyperbole [accessed 09 November 2013]

Lambrecht, A., & Tucker, C., (2013) ‘When Personalised Ads Really Work’ [online], available: http://blogs.hbr.org/2013/06/marketers-serve-no-ad-before-i/ [accessed 13 November 2013]

Lee, Y., (2012) ‘New marketing rules for the age of personalisation’, [online], available: http://www.adexchanger.com/data-driven-thinking/new-marketing-rules-for-the-age-of-personalization/ [accessed 08 November 2013]

IBM, (2013) ‘Customer Enhanced 360 degree Image’ [online], available:
http://www1.ibm.com/software/data/bigdata/use-cases/enhanced360.html [accessed 01 November 2013]

IBM (2013) ‘Trident Marketing- Increases revenue nearly 1,000 percent with predictive analytics’, [online], available: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=AB&infotype=PM&appname=SWGE_IM_ZN_USEN&htmlfid=IMC14755USEN&attachment=IMC14755USEN.PDF [accessed 09 November 2013]

Grealish, H. (2013) ‘Finding new ways to retain consumer love…in the ‘like economy’, Presentation 12 Feb 2012, Smurfit Business School, available: UCD Blackboard [accessed 2 May 2013]

McAfee, A. & Brynjolfsson, E., (2012) ‘Big Data the Management Revolution’, Harvard Business Review, Oct 2012, available: UCD Ebsco Host Database [accessed 12 October 2013]

McGuire, T., Manyika, J., & Chui, M. ‘Why Big Data is the new competitive advantage’, Ivey Business Journal, July/Aug 2012, available: http://iveybusinessjournal.com/topics/strategy/why-big-data-is-the-new-competitive-advantage#.UnzZIXBSj4s [accessed 07 November 2013]
MGI & McKinsey (2013) ‘Big Data: Deep analytical talent: Where are they now?’ [online], available: http://www.mckinsey.com/features/big_data [accessed 07 November 2013]

McKinsey (2009) ‘The Consumer Decision Journey’ McKinsey Quarterly, June, available: http://www.mckinsey.com/insights/marketing_sales/the_consumer_decision_journey [accessed 11 November 2013]

Polonetsky, J., & Tene, O., ‘Privacy and Big Data’,66 Stan. L. Review. 25, available: http://www.stanfordlawreview.org/online/privacy-and-big-data/privacy-and-big-data [accessed 08 November 2013]

Sicular, S., (2013) ‘Gartner’s Big Data Definition Consists of Three Parts, Not to Be Confused with Three “V”s’ Forbes, 27 March 2013, available: http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/ [accessed 21 October 2013]

Ward, J., & Barker, A., (2013) ‘Undefined By Data: A Survey of Big Data Definitions’ [online], available: (http://arxiv.org/pdf/1309.5821v1.pdf) [accessed 28 October 2013]

Webster, K., (2008) ‘The Marketers Guide to Descriptive V’s Predictive Modelling’ [online], available: http://www.accudata.com/wp-content/uploads/WP-Descriptive_v.-Predictive.pdf [accessed 12 November 2013]

Cathal Quinn
Share This