Digital Marketing Foundations: Merging Traditional & Digital Marketing

Successful websites do three main things:

Generate traffic
Drive conversions
Maximize revenue per conversion

The textbook describes how the AIDA model can be applied to support websites, and make them successful. Depending on where the consumer [visitor] is in their journey, will depend on how they arrive on the website, how they convert, and how much revenue can be generated. The AIDA model is broken down further into the psychological underpinnings which align with each stage in the model (Kotler & Keller, 2012; Marshall & Johnston, 2019).

Although there are other response hierarchy models such as the hierarchy of effects model, the innovation adoption model, and the communications model, the AIDA model best aligns with the principles of digital marketing, web acquisition, and successful websites (Kotler & Keller, 2012).

Consumer Behavior & the Consumer Purchase Decision Process

The AIDA model outlines a continuum for how consumers [visitors] become aware, become interested, and move into the purchase of a product online – or some other macro conversion. A model that aligns, and works in parallel to the AIDA model is the consumer purchase decision process. The image below includes the internal and external factors, as well as the self-concept and lifestyle constructs that are the underlying consumer behavior foundations that drive consumers into the decision process.

Source: (Hawkins & Mothersbaugh, 2016)

The consumer purchase decision process outlines how consumers progress towards a purchase [macro/micro conversion] commitment, and their post-purchase behavior. Since the situation for digital marketers is in the online environment, this discussion will focus on the decision process from the point-of-view of an online situation. It’s important to note that the digital marketer must always be at the table when discussing off-line campaigns. If the call-to-action (CTA) is to drive targets to the website, the URL needs to be properly identified, and any tracking factors need to be confirmed. More on this in week 2.

Digital Marketing & the Consumer Decision Process

Once the consumer identifies a problem – a delta between their current state and their desired state – they begin with an information search (Hawkins & Mothersbaugh, 2016). From an online perspective, consumers are likely to start their information search using a search engine. If consumers are aware of a specific solution to their problem, they may visit a website directly, or may be motivated by a promotional email they receive, but if they are not aware, the websites that appear in the search results for their given query become the sites that they are now aware of; this can include the paid ads at the top of the search engine results page (SERP), local listings, rich snippets, and organic results. The anatomy of a SERP has drastically changed from a few years ago. The organic listings continue to be pushed below the fold, as premium digital real-estate is consumed by ads, structured data, and local listings. More on this in the weeks to come.

Anatomy of a Search Engine Results Page

Consumer’s don’t necessarily visit a site and immediately make a purchase. In many cases, they may visit a few sites, and depending on the level of involvement, they might extend their information search and alternative evaluation over an extended period of time. In many cases, the path to the site may take multiple avenues such as search, display/banner ads, social, and direct. This type of behavior is referred to as attribution.

The information search and alternative evaluation levels of the decision process most closely align with the interest and desire levels of hierarchy in the AIDA model. More specifically, in the alternative evaluation and desire phases, consumers may find themselves using online reviews, talking with current consumers, investigating product review blogs and videos, and conducting their own price comparison. Digital marketers can nurture leads at this stage of the models using many techniques. Some examples include:

Paid Search
Display Ads
Video
SEO
Social – Paid & Organic
Content Marketing
Email
Retargeting
A/B testing

Before progressing further, the concept of Inbound Marketing should be injected into this conversation

AIDA, the Consumer Decision Process, & Inbound Marketing

For good measure, let’s throw in a third model c/o Hubspot, but first, let’s define Inbound Marketing. Inbound marketing is “a fundamental shift in the way business is conducted. It is a philosophy based on helping people. The inbound approach to doing business is more human and customer-centered” (Thibeault, Kilens, Burke, Halligan, & Elworthy, 2018). While outbound marketing is more invasive, more interruptive, inbound marketing seeks to pull consumers in, nurture them, build a relationship, and maintain that relationship. Outbound marketing methods force-feed communications to consumers by interrupting them with TV ads, radio ads, billboards, print, and product placement. Inbound marketing methods seek to support the consumer’s journey through the decision process by nurturing them as they progress along the continuum, and continue to do so after the commitment has been made.

The Inbound Methodology

Source: (Thibeault, Kilens, Burke, Halligan, & Elworthy, 2018)

The action phase of the AIDA model refers to when the consumer converts. This aligns with the outlet selection and purchase in the consumer decision process, and the close phase in the inbound methodology. While the AIDA model stops at the action phase, there is another step in both the consumer decision process and inbound methodology; post-purchase evaluation is where the consumer confirms whether their choice has helped them achieve their desired state (or not), and delight is where the digital marketer should be maintaining the relationship to confirm consumer satisfaction, or take action where there is a disconnect between actual and desired states. From an inbound marketing perspective, this is where the online reviews, feedback, and word-of-mouth can make or break the digital marketer’s success. The trickledown effect of a bad review from a consumer whose journey has progressed through all phases of the models, is the impact it will have on consumers who are in the information search and alternative evaluation phases of their journey. Good reviews can motivate and support decision making, while bad reviews can detract consumers from committing. Digital marketers need to keep this in mind, and use this knowledge to their benefit as the plan, improve, and measure their digital campaigns.

The use inbound marketing, and the inbound marketing methodology in tandem with the AIDA and consumer decision process models will make websites successful, and support the consumer journey. Below we discuss briefly how these models, and more specifically, how the AIDA model can be used to impact traffic, conversions, and revenue maximization.

How the AIDA model impacts Traffic

There are three main types of traffic:

Direct
Referral
Search

Direct Traffic

Direct traffic consists of visitors who visit a site directly. This implies that consumers are aware of the website/business. Direct traffic sources include typing the URL directly into the browser window, using bookmarks, and clicking a link from an email (Larson & Draper, 2018). Avinash Kaushik (2010) suggests that direct traffic can be great, but if you are spending significant amount of money on non-digital advertising, it can be a very expensive form of traffic acquisition. When looking at the consumer purchase decision process, visitors that arrive direct to the website have likely already visited the website, and are possibly in the alternative evaluation, or purchase phase of the decision process; it’s possible some may have already purchased and are returning visitors.

Referral Traffic

Referral traffic consists of two different types, paid and unpaid. The paid subcategory of referral traffic includes video, display, banner, and social media paid advertisements. The unpaid subcategory includes inbound links from third party sites, and traffic from links that are non-paid within social media. Content marketers aim to add value by using blogging, guest posting, and link acquisition, while social media referral traffic can be generated using a business page, personal posts, and for those lucky ones, from influencers that share posts and they end up going viral.

Search Traffic

Search traffic consists of visitors who arrive at a site via search engine. There are two types of search traffic: (1) paid, and (2) organic. Paid traffic is generated using search ads predominantly in Google or Bing. Organic traffic is generated from visitors who conduct a search in a search engine, and click on one of the organic search results in the SERP(s). Visitors who arrive at a site via search, are most likely in the problem recognition and information search stages of the consumer decision process (Hawkins & Mothersbaugh, 2016).

Digital marketers need to identify which stage of the AIDA model, consumer decision phase, and inbound methodology their visitors are in. The ability to make this distinction can provide valuable insights towards the chosen web acquisition strategy. For example, if analytics reveals that the majority of traffic is arriving from search, the marketer can infer that visitors are not aware of the site, and are in the information search phase of their purchase journey. The marketer may want to segment this data by search engine to determine which, if any, search engine is providing more traffic than another. Possibly one of the search engines is not contributing to this traffic? This may lead the marketer to investigate what types of keywords are being used, and the websites ranking for those keywords. This can be achieved using the queries report in Google Search Console (GSC), and Bing webmaster tools. Both will provide ranking and keyphrase information. How is the website ranking? Good? Bad? Both? According to an article on Moz from 2014, the click-through rate (CTR) of organic listings in several search engines (excluding Google and Bing) saw a significant drop-off after the fifth position (Petrescu, 2014). Based on the outcome of the keywords analysis and ranking, the marketer might determine their website does not rank very well for some, or all, of the key phrases. They may decide to purchase some paid search ads for those specific key phrases so their website can appear at the top of the SERPs, and they expand their real-estate in the SERPs with both paid and organic listings. This will also provide the marketer with important data such as click-through rate (CTR), and quality scores for landing pages. The marketer can use this info to identify the best converting key phrases, and determine which landing pages are best optimized for the selected key phrase(s). The digital marketer may also conduct some searches of the target key phrases to examine the results, and identify what other types of listings are showing in the SERP. Using the AIDA model in conjunction with the consumer decision process model is a valuable tool that can be used to analyze web traffic, and make adjustments to web acquisition strategies.

How the AIDA model impacts Conversions

Larson and Draper (2019) suggest that a conversion is an action that you want visitors to take (p.16). Conversions don’t necessarily have to be monetary. As Kaushik (2010) suggests, marketers need to plan for both macro and micro conversions. While the macro conversion may be completing a sale, or downloading a file, micro conversions can include the amount of pages visited, time on site, or watching a video for a specific amount of time. Micro conversions can lead to macro conversions. Digital marketers need to include a measurement plan as part of their planning strategy. Marketers need to reverse engineer their strategy by identifying their macro and micro conversions, which metrics provide the best indicator of success to each conversion type, how often they will take measurements, and the plan for website development, code updates, and other communications updates to align with the plan.

Attribution Modeling

Attribution modeling “is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths” (Google Analytics, 2018). There are several different types of interactions in attribution modeling, they include:

Last interaction – the last touchpoint receives 100% credit for the sale
Last non-direct interaction – direct traffic is ignored; last channel visit receives 100% credit for the sale
Last Ads click – last touchpoint from paid channel receives 100% credit for the sale
First interaction – the first touchpoint receives 100% credit for the sale
Linear – all touchpoints receive the same amount of credit for the sale
Time decay – touchpoints closer to the sale receive more credit for the sale than those further away
Position based – 40% credit for the sale is assigned to both the first and last touchpoints

(Google Analytics, 2018)

What can we derive from attribution modeling? Once marketers implement the proper tracking JavaScript code, they can track attribution of visitors. The marketer then has the ability to segment attribution data to investigate traffic sources, attribution paths, and numbers of touchpoints. They can also investigate the time from first touchpoint to last touchpoint and determine the typical duration to make a sale. For example, the marketer may identify that it takes four touchpoints on average to generate a sale (conversion) on their site. They investigate the traffic source by each touchpoint and learn that the average visitor makes their first visit to the site via search engine, their second visit via unpaid referrer, and their third and fourth visits direct. On average, the marketer identifies a typical sale takes 30 days from start to finish. What can the marketer do with this data? First, they can identify the channels which align to each stage in the AIDA model and purchase decision process. They may also be able to discern how the consumer moves through the decision process with respect to the inbound methodology and identify potential nurturing opportunities. They may identify that the visitor might have converted faster than 30 days if they had reached out to them with an email campaign after the first direct visit. They might also consider using a retargeting campaign to reduce the duration to conversion by placing ads in front of the consumers four days after their last visit to the site. This might remind consumers, and possibly motivate them to take action sooner. Marketers may also investigate the visitor flow to determine whether there is a specific conversion path that visitors are using to achieve the conversion goal.

How the AIDA model Supports Maximizing Revenue

Maximizing revenue aligns with the post-purchase behavior and delight phases of the decision process and inbound methodology. The AIDA model can be recycled as consumers identify further gaps between their current and desired states, and hopefully return to the same website to make more purchases. The inbound methodology of delight aims to convert customers into promoters which keeps those customers coming back, but also increases the potential for word-of-mouth to influence other consumer’s to also purchase from the same site.

Customer Lifetime Value

Customer lifetime value (CLV) is a unit of measure that is used by customer centric firms to quantify customer equity; “it is the present value of future cash flows associated with a particular customer” (Fader, 2012, p. 71-72). Marketers can use their sales data, web acquisition costs, and a repeat purchase rate to calculate CLV. While the discussion of CLV is beyond the topic of this discussion, it is worth investigating the various ways to calculate CLV and the use of the recency frequency model (RFM) to aid them in their pursuit to maximize revenue, and optimize web acquisition costs.

Machine Learning

Recommender engines such as the one that Amazon uses is based on using predictive modeling and consumer purchase data to cluster purchase behavior together, and provide product recommendations to existing customers based on users that made similar purchases (Miller, 2015). This is another form of revenue maximization strategy.

Consumer Behavior Models & Revenue Maximization

Digital marketers who understand, and can identify, where consumers are along the continuum of the AIDA model, the consumer purchase decision process, and the inbound methodology, can better amplify their strategy to reduce churn, and increase revenues. Digital marketing and data science together provide the digital marketer with tools to analyze, strategize, implement, measure, and revise their marketing strategies so that traffic is increased, conversions are improved, and revenue is maximized. For example, on an ecommerce site, the attribution model may suggest that returning visitors are visiting a specific set of product pages. The marketer can devise a retargeting campaign with the destination page as those highly visited product pages. The campaign will serve ads to visitors who visit those exact pages four to six days after their last visit. If the marketer really wants to stimulate purchase, they may create a retargeting campaign in conjunction with a price promotion (say 15% off), and an urgency call to action in the copy of the display ad which motivates consumers to move from the interest/desire phase of the AIDA model, to the action phase.

Digital Marketing Foundations in Summary

You may have started to notice how connected digital marketing is to the foundations of marketing management. Digital marketers can succeed by applying digital marketing tactics with strategies founded in the traditional roots of strategic marketing and consumer behavior.

Successful websites generate traffic, convert, and maximize revenue. The AIDA model describes the series of steps a consumer takes on their journey to converting. By cross-referencing with the consumer decision process, marketers can gain a greater understanding about the consumer’s mindset as they progress through the AIDA stages. Digital marketers that overlay the inbound methodology on the AIDA and decision process models, will find themselves better equipped to design web acquisition and lead nurturing strategies.

The AISAS Model

As I conclude this overview of digital marketing, I came across an expansion of the AIDA model which appears to merge the inbound methodology with the AIDA model that we have discussed above. Marissa Chantamas and Kitima Pongsatha (2017) from the University of Thailand have derived the AISAS model from the AIDMA and AIDA models. The AISAS model includes the final share stage which would align with the post-purchase evaluation and delight stages discussed above. This reinforces the importance of maintaining the relationship with consumers after the sale, as it is a key factor to generating recurring revenue.

Source: (Chantamas & Pongsatha, 2017)

Over the next seven weeks, we will learn how to analyze website metrics, and tactics that can be used to improve traffic, conversions, and revenue maximization. The key to being an effective digital marketer lies in your ability to expand your knowledge of using digital tactics, while maintaining the strategic marketing foundation that has been developed in the brand management, consumer behavior, communications, research, and marketing foundations courses.

If I missed something, or you want to add to the discussion, feel free to comment below.

References

Chantamas, M., & Pongsatha, K. (2017, August 21). AIDA to AISAS –the New Theory for Understanding Consumer Responses to Communications. Retrieved from Department of Marketing: University of Thailand: http://www.marketing.au.edu/our-department/kms/407-aida-to-aisas-%E2%80%93the-new-theory-for-understanding-consumer-responses-to-communications.html

Chernev, A. (2018). Strategic Marketing Management (9th ed.). USA: Cerebellum Press.

Fader, P. (2012). Customer Centricity (2nd ed.). Philadelphia, PA, United States: Wharton Digital Press.

Google Analytics. (2018). Attribution modeling overview. Retrieved from Analytics Help: https://support.google.com/analytics/answer/1662518?hl=en

Hawkins, D. I., & Mothersbaugh, D. L. (2016). Consumer Behavior: Building Marketing Strategy (13th ed.). New York, New York, United States: McGraw-Hill Irwin. Retrieved April 11, 2018

Kaushik, A. (2010). Web Analytics 2.0. Indianapolis, IN, United States: Wiley.

Kotler, P., & Keller, K. L. (2012). Marketing Management (14th ed.). Upper Saddle River , New Jersey, United States: Prentice Hall. Retrieved December 5, 2015

Larson, J., & Draper, S. (2018). Digital Marketing Essentials. Rexburg, Idaho, United States: Edify.

Marshall, G. W., & Johnston, M. W. (2019). Marketing Management (3rd ed.). New York, NY, United States: McGraw-Hill.

Miller, T. W. (2015). Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python. Old Tappan, NJ, United States: Pearson Education Inc.

Petrescu, P. (2014, October 1). Google Organic Click-Through Rates in 2014. Retrieved from Moz.com: https://moz.com/blog/google-organic-click-through-rates-in-2014

Thibeault, L., Kilens, M., Burke, K., Halligan, B., & Elworthy, A. (2018). Inbound Fundamentals. Retrieved from Hubspot: https://app.hubspot.com/learning-center/4643436/tracks/24/intro