Digital Enterprise. White Paper. Multi-Channel Strategies that Deliver Results with the Right Marketing Attribution Model

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1 Digital Enterprise White Paper Multi-Channel Strategies that Deliver Results with the Right Marketing Model

2 About the Authors Vishal Machewad Head Marketing Services Practice Vishal Machewad has over 13 years of experience in sales and marketing, having worked as a functional consultant and Subject Matter Expert (SME) in marketing, customer experience, digital and interactive marketing, web analytics, social media and advanced analytics. He currently heads the Marketing Services Practice at TCS and provides thought leadership in the Marketing Management domain. He has led and managed marketing solutions and delivery across diverse industry segments in the US and in India. Machewad has an MBA in Marketing Management and a Bachelor's degree in Engineering from the Pune University, India. Poorva Inamdar Digital Marketing Analyst Poorva Inamdar is part of the Digital Enterprise Solutions and Services unit at TCS. With over six years of marketing experience, Poorva has built expertise on customer and marketing behavioral analytics, digital analytics, and multichannel campaign management. She is currently working on marketing automation and optimization of campaign management. Inamdar is a Post Graduate in Operations Research from the University of Southampton, UK. Her statistics and operations research and ability to apply advanced statistical concepts allow her to significantly contribute to diverse marketing areas. Vina Pai Vernekar SAS Developer Vina Pai Vernekar works with the Digital Enterprise Solutions and Services unit at TCS. Vernekar has over seven years of experience in monitoring and enhancing cross-channel marketing campaigns, and aligning them with business objectives. She has a strong understanding of the marketing function and experience across multiple domains such as life sciences, banking and financial services.

3 Abstract With the rapid strides in technology and innovation delivering improved accessibility, customers today frequent various channels when buying a product. Some channels influence them more than others. However, the increased number of touch-points make it challenging to assess the role each of these channels play in influencing and driving sales. Marketers are often unable to prioritize the marketing spend on these channels in a way that accounts for their ability to influence and drive sales. Given customers' increasing preference to switch channels while transacting, attributing a conversion to the channel where the transaction was completed can offer misleading or incorrect insights. Organizations can end up redirecting investments and marketing strategies to channels that, in reality, have little influence over their customers' experience and buying decisions. Capturing the contribution from various channels, both online and offline, can help design a marketing strategy that optimizes marketing spend. This paper looks at some of the most popular marketing attribution models. We discuss how multichannel analytics contributes to increased sales and how it can be used to optimize marketing spend. We also recommend regression techniques for developing an effective marketing spend strategy prior to an existing and new product launch.

4 Contents Optimizing marketing ROI in a multi-channel environment 5 Finding the best-fit model: The different types of marketing attribution models 8 Assigning credit where it matters the most 8 The five step methodology to get multi-channel attribution right 8 Orchestrating the perfect multi-channel marketing program 10

5 Optimizing marketing ROI in a multi-channel environment The consumer journey is more complex today. With the proliferation of digital platforms and online and offline channels, the consumers path to purchase is no longer linear - it transcends channels and devices. Each of these channels generates vast amounts of data. Marketing attribution models can help companies analyze this data and identify the channels that yield optimal return on investment (ROI). These models assign values or credit to different channels based on the extent they influence consumer purchase behavior. The higher the value, the greater is the likely contribution from that channel. also offers the ability to understand the impact of various channels on the entire sales cycle and leverage them to optimize the marketing spend. Over time, backed by technology and analytics, many of the attribution methods have matured, allowing companies to migrate to a multi-click and cross-funnel view of the final purchase. From justifying to optimizing digital spend and rethinking the channel mix to improve performance, attribution models are proving critical for marketers across industries. By applying the right attribution model, marketers will be able to answer strategic questions on the ideal distribution of budget across advertising and marketing channels, as well as identify the channel that maximizes returns. By adopting the appropriate attribution model, companies can direct their spending and messaging to maximize influence and improve their chances of reaching the right customers, at the right time and place, with the right message. 5

6 Finding the best-fit model: The different types of marketing attribution models As shown in figure 1, there are different types of attribution models that help companies assign values to the channels used to market a product or service. 1. Last Interaction 6. Multi-Channel 2. First Interaction MOdels 5. Position based 3. Linear Interaction 4. Time Decay Figure 1: Types of attribution models Each of the discussed below models has its own advantages and limitations: 1. Last interaction attribution model This is the most widely used model - the default option in many cases - and is also used to benchmark the results of other models. This attribution method assigns all credit to the last or final advertisement clicked by the customer. However, since the consumer goes through a number of channels before the actual conversion, it is not accurate to assign maximum value to the last channel. The gaps in this model could cause advertisers to invest their funds in wrong channels, leading to an adverse impact on the revenue and marketing inefficiencies. Figure 2: Last click attribution 6

7 2. First interaction attribution model This model assigns all the credit to the first advertisement clicked by the customer. Such an approach may be useful for advertisements and campaigns that aim to build brand awareness. In such cases, this model helps identify the channels that first exposed consumers to the brand. Similar to the last channel attribution model, this model is rather myopic in its view when assigning values and does not account for subsequent marketing effort. In reality, secondary channels can play a far more important role in converting a potential customer into an actual customer. Figure 3: First click attribution 3. Linear interaction attribution model This model assigns equal credit to every channel used in the customer journey - from brand awareness to conversion. It is useful for companies that want to target consumers at every stage of their decision cycle to accelerate the journey to conversion. While this model may appear to be more holistic as compared to the previous two, in reality, one channel may influence the consumer more than the others. By considering all channels as equal, companies may over- or under-allocate resources to each channel. Figure 4: Linear click attribution 4. Time decay attribution model This model assigns greater value to the channel that is used closest to actual conversion. This can be particularly useful when an organization runs a one or two-day campaign, in which case it is logical to give more credit to the primary channel used during the campaign. Channels used before the campaign get less credit. The flaw with this strategy is that initial advertisements may have been instrumental in building awareness, making the consumer more receptive to the campaign. By discounting the impact of previously used channels, this model may offer an unbalanced view of channel contribution. Figure 5: Time decay click attribution 5. Position-based attribution model A hybrid of first and last channel attribution models, this model gives more credit to the first and last channels. Organizations from the retail, airline, and BFS sectors, for whom the initial and closing channels are most important, find this model very useful. However, in the real world, the consumer may have been influenced by other channels - a fact this model overlooks - and may lead to inefficient allocation of resources. Figure 6: Position based attribution 7

8 6. Multi-channel attribution This model assigns credit to all the channels according to their contribution along the consumer s path to conversion. This is a data-driven approach, and the most scientific and advanced attribution model. It gives a holistic picture of the influence of each channel and helps the organization allocate and optimize resources to increase revenue. Figure 7: Multichannel attribution Assigning credit where it matters the most It is evident that a combination of complex consumer behavior and their ability to interact across channels places greater emphasis on sophisticated and data-driven decision making. Multiple channels along the customer's purchase path such as , paid search, TV, radio, newspapers, and others have the power to influence a buying decision. By undertaking attribution analysis, advertisers gauge the impact of specific marketing activities on campaign results. The last channel model gives credit only to the last clicked advertisement and does not take into account s and paid searches that significantly contribute to revenue and are also key areas of investment for many companies. This model may also fail to measure the impact of digital channels and offline channels such as television and print media on conversions. From this perspective, the multi-channel attribution model is a better option since it identifies the customer purchase pattern across channels. By using a data-driven approach pattern identification, it helps companies optimize marketing strategies to maximize profit. For instance, this model can determine if an campaign has the same influence as a paid search. When used in campaigns, this model assigns more credit to the channels that create demand and influence customers further up the sales funnel. Though there has been progress in the development of multichannel attribution models, there is no existing accepted framework or standard of Marketing and Channel success measurement. One of the ways this can be done is through a regression model to analyze the multichannel attribution of different advertisements. The coefficients of covariates and interaction terms help analyze the influence of each advertisement on the customer. Many tools available in the market can help measure attribution. Google Analytics provides a free model comparison tool, which assigns credits to channels that play a role in conversion. Other online marketing tools such as Media Mind, Omniture, and Tag Man also help in measuring marketing attribution. The five step methodology to get multi-channel attribution right Several industries such as retail, insurance, banking, travel, manufacturing, and others can adopt a framework for multi-channel marketing attribution. Figure 8 represents the multi-channel marketing attribution framework which 8

9 maps the customer s journey, identifies influential factors, and uses statistical techniques to zero in on the channel that generates the maximum ROI. External Factors Customers Influencing Factors Demographic forces Cultural factors Inbound Channels Outbound Channels Social factors Personal factors Emerging Channels Pricing 1 Sequence Time frame Multi Communication Marketing Management Implement Model 3 Conduct Statistical Analysis Best Marketing Model Leverage Model 2 First Channel Multichannel Last Channel Position Based Linear Time Channel Figure 8: Marketing attribution framework Here are five steps for effectively implementing the marketing attribution methodology: 1. Identify and analyze channel data: As a first step, data from various channels must be gathered to understand how it relates to the business problem statement. This is followed by identifying the factors that influence customers purchase behavior, taking into account demographics and psychographic factors as well as internal factors such as pricing to gain better insight into buying decisions. 2. Align periodically: Time is an important factor to consider when applying the attribution model and will help account for the length of the sales cycle to improve attribution accuracy. In this step, the touch points that were first exposed to the customer need to be identified. These must be aligned with the sequence of events in the path to conversion. 3. Implement the model: The correct model should be applied based on the analysis of customer behavior data and the impact of various marketing channels. The chosen model should be validated periodically, say every six months, to accommodate changes in customer behavior due to internal or external factors. 4. Conduct statistical analysis: For complex data, sophisticated statistical techniques such as linear, multiple, and logistic regression or econometric models should be used. Statistical analysis can be carried out using software tools such as Statistical Analysis System, SPSS or R software. The analysis helps identify trends in data, solve business issues, analyze customer behavior, and assess the performance of various marketing channels. 9

10 5. Evaluate and optimize: Output from the statistical analysis can be used to understand which marketing channel has the greatest influence on customers purchase decision. This is the channel with the highest value of regression coefficient. The output aids in strategic decision making with respect to investments in various channels. It may help identify strategies to continue with existing channels, leverage new ones, or reallocate the budget across channels to further improve ROI. Orchestrating the perfect multi-channel marketing program Companies are increasingly investing in diverse channels, and have high expectations from multi-channel programs. Whether it is traditional marketing, social media, or a paid search, the goal is to reach the customer before competition and ultimately improve conversions. Marketing attribution has become one of the key methodologies to ensure that both short-term campaigns and long term marketing strategies deliver the desired results in terms of ROI. In today s omni-channel marketing environment, it is clear that marketers need to move beyond traditional attribution models and embrace a data-driven approach in order to optimize their marketing spend. Every channel influences purchase decisions in different measures for different customers, and understanding the complete sequence of events that lead to conversion is critical to evaluating each channel s effectiveness. As multi-channel marketing attribution matures, marketing campaigns are likely to deliver better results by leveraging the right, contextually relevant channel. Instead of evaluating consumer behavior in isolation, by mapping it with their interactions across channels, in combination with factors such as price and brand affinity, marketers will be able to create a more holistic and effective marketing strategy. 10

11 About TCS' Digital Enterprise Unit The TCS Digital Enterprise unit applies the Digital Five Forces Mobility and Pervasive Computing, Big Data and Analytics, Social Media, Cloud, and Artificial Intelligence & Robotics to meet the unique needs and opportunities of each industry. We help clients reimagine their business models, products and services, customer segments, channels, business processes, and workplaces by leveraging a combination of the Digital Five Forces to gain sustained competitive advantage. Our experienced global team includes business strategy consultants, business analysts, digital marketers, user experience designers, data scientists, and engineers who are passionate about today's digital technologies and their impact on businesses. Backed by our technology vendor partnerships, pre-built customizable products and reusable assets, and deep industry expertise, we offer everything an enterprise needs for complete Digital Reimagination. Contact For more information about the TCS Digital Enterprise unit, visit: Subscribe to TCS White Papers TCS.com RSS: Feedburner: About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and TM assurance services. This is delivered through its unique Global Network Delivery Model, recognized as the benchmark of excellence in software development. A part of the Tata Group, India s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India. For more information, visit us at IT Services Business Solutions Consulting All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2015 Tata Consultancy Services Limited TCS Design Services I M I 04 I 15

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