Pricing Personalization, Net Revenue Optimization & Marketing Interventions
As we approach the holiday season of 2023, let's explore the challenge of addressing the empowered consumer. It's readily recognized customers are digitally savvy, discerning and motivated to get the best deal. This has made it increasingly difficult for brands to develop pricing strategies that optimize net revenue.
Customer-centric pricing can be a game-changer. Whether an online or brick-and-mortar shop, pricing personalization aims to use data and analytical insight to influence what is (or isn't) offered to each prospective buyer. Is the price a part of the shopping experience? Absolutely. Price is one of the major factors that forge a consumer's buying decision and loyalty.
The direct interaction between brands and customers (think web, mobile app, email, etc.) enable the opportunity to implement personalized pricing more effectively. Brands collect 1st party data on the consumer's engagement with their product offerings, and can use this information to develop offer strategies. Over time, our consultative engagements here at SAS with various brands have shown common challenges:
- Storage and effective use of high quality, un-sampled 1st party data on user behavior, purchase history, etc.
- Optimizing net revenue with pricing tactics
Pricing personalization can be a useful tool to help brands reach different objectives, depending on their business model, market conditions, and customer segments. For instance, brands can increase sales volume by targeting price-sensitive customers with lower prices while maintaining higher margins from less price-sensitive consumers. Sounds logical, rationale and intelligent, right? While pricing is often described as a science by practitioners, it is a key factor to drive conversions. And we want to do this while optimizing profit, which points to a secondary challenge in regard to which customers should receive a marketing intervention (or stimuli), and which ones shouldn't.
Setting the right price for a good or service is an old problem in economic theory. Keep in mind, there are a vast amount of pricing strategies in existence that depend on the objective sought. One brand may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments. Although strategies like premium and penetration pricing have existed for many years, let's focus on the use of algorithms to address this challenge.
Algorithmic personalized pricing is a process of setting optimal offers using the power of machine learning and artificial intelligence to maximize revenue, increase profit or address other business goals set by brands. Algorithmic personalized pricing is one of the most powerful means of gaining a competitive advantage.
Introducing SAS PROC DEEPPRICE to personalize prices and optimize revenue
One challenge brands will always face relates to the heterogeneous characteristics of their customers. Such variability can affect how customers choose to (or not to) convert from a targeted marketing intervention (such as a price increase or decrease). Understanding customer response behavior to targeted offerings is crucial for informing individualized pricing decisions. PROC DEEPPRICE offers a flexible framework for specifying and estimating customer responses to marketing treatments.
PROC DEEPPRICE enables SAS users to specify customer treatment effects (or marketing tactics) as unknown functions of observed customer behaviors. For readers who are finding this technical, let's break this down.
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A ‘customer treatment effect’ is the causal effect of a variable (like price) on a target outcome (like purchase conversion). The term ‘treatment effect’ originates in medical literature concerned with the causal effects of yes-or-no ‘treatments’, such as an experimental drug or a new surgical procedure. The term is now used much more generally, and can be applied in marketing and customer experience.
- 'Unknown functions of observed customer behaviors' originates from data science and specifically nonparametric regression which is a category of regression analysis in which a predictor (such as a customer behavior signal) does not take a predetermined form (like linear or quadratic regression) but is constructed according to information derived from the data itself. That is, no parametric form is assumed for the relationship between predictors and target outcome variable.
Moving on, PROC DEEPPRICE uses Deep Neural Networks to estimate unknown functions. In other words, SAS is providing users (analysts/data scientists who support customer experience use cases) native Marketing AI capabilities through deep learning to overcome obstacles in improving revenue-centric KPIs. Although other software vendors like to broadcast how they have AI capabilities, there is much more to explore and benefit from the discipline beyond simply generative AI (which is a subset of what AI is capable of).
We believe the 2023 incremental excitement surrounding AI has flipped the marketing industry's perspective upside down in how to benefit from this phenomenon. The most challenging problems in marketing (and enterprises) can be addressed with analytical innovation and best practices across the entirety of AI capabilities.
Pivoting back, such a rich and flexible approach through PROC DEEPPRICE accounts for individual customer variability in treatment responses providing the advantage of extracting clear insights from complex forms of heterogeneous behaviors (even with large data sets). It enables brands to determine, for example, what types of consumers are price sensitive (and which aren't).
PROC DEEPPRICE enables users to perform policy analysis to compare outcomes under various hypothetical treatments. For those unfamiliar with this term, 'policy analysis' is the process of identifying potential options that could address a business problem and then comparing those options to choose the most effective, efficient, and feasible one. For example, it can answer questions such as:
What is the best pricing strategy to optimize revenue?
Subsequently, marketing teams can leverage these insights for pricing personalization and improving revenue metrics.
Use Case & Demo: Online Media Brand
Let’s look at an example of how an online media brand can offer targeted discounts through personalized pricing to optimize revenue. The data set is provided by the Microsoft research project ALICE. For more details, see Example 15.1 in the SAS Help Center documentation. The data set has 10,000 simulated observations that represent user personal characteristics. Also taken into account is user online behavior history, including previous purchases and previous online times per week. The treatment variable, price, is the price the customer was exposed to during the discount season. The outcome variable, demand, is the number of songs that the customer purchased during the discount season. The image below summarizes the analysis table through showing the names of the variables that are used in the model, along with their type and definition.
Now, let's summarize our intention with this data table. This use case example has three goals:
- Estimate the parameters (or customer signals) of interest, such as average partial effect (or influence) of price on demand. In other words, we want to understand how much influence pricing has on purchase conversions.
- Estimate the price elasticity of demand for product offerings (songs), and assess its variation with customer characteristics. The translation here is what impact will pricing adjustments (increase on decrease) have on purchasing behavior.
- Conduct a policy analysis that aims at increasing revenue despite decreasing the purchase price for some customers. The analysis results below will show eight different pricing strategies across a customer universe and determine which policy (or scenario) generates the most revenue.
Next, the following Flow Swim Lane in SAS Studio (Image 4 below) contains initial steps which estimate the effect of price on the demand for songs purchased and the details of the estimation are used later for strategic pricing policy evaluation.
One output from the analysis Swim Lane shown below in Image 5 is the estimated average slope. It represents the average impact of price on purchase demand for all the customers in this data. It is negative (-9.78) and statistically significant. This is an expected result in accordance with economic theory as many readers will recognize that as the price of items increases, demand decreases.
Another customer data signal of interest is the price elasticity of demand, which measures the degree of sensitivity of demand to price. In general, demand decreases when price increases for most products, but the amount by which demand decreases is greater for some products than for others. PROC DEEPPRICE can be used to compute individual-specific elasticities. It is of particular interest to assess how price elasticity varies among customers with respect to their income level (as well as other personal attributes).
Figure 6 above shows a remarkable discovery among two sub-groups of homogeneous customers with respect to income and their response behavior related to a price increase. Customers whose income segment (portrayed on the x-axis) is less than 1 (representing ~51% of all customers in this data) are more sensitive to a price increase; if price goes up (for example, by 1%), their number of songs purchased falls by 1% to 5%, compared to ~ 0.15% decrease for higher-income customers. Right away, from a marketing and pricing perspective, the segmentation insights are profound. One group is exclusively price insensitive with elasticity close to zero where discounting would be wasteful. The second group has a higher variance and highlights the price-sensitive customers where discounting can be effective.
Analysts can explore the relationship with other customer attributes to collect additional insights. For example, the variability of price elasticities with income and days_visited is shown in Figure 7. The variable days_visited represents the average number of days per week a customer engages with the brand's website. Low-income customers who visited the brand's website less often (days_visited <5) are even more price-sensitive than low-income customers who visited the website more often (days_visited >=5). The latter group has a price elasticity between –2 and –1, compared to a range of –5 to –1 for the former. This information is vitally important for targeting customers with (or without) a price discount.
Translating Noisy Heterogeneity From Price Effects Into Actionable, Personalized Prices
In policy optimization, the goal is to maximize the expected utility of a policy. For the definition of the expected utility function and how it is estimated, as well as details such as the definition of a policy rule, see the SAS Help Center page summarizing the function.
In our working example, the policy decisions are to choose customer-specific prices in order to maximize the revenue, which is defined as:
This particular formula for pricing in Image 8 seeks to maximize revenue in a personalized manner, but can exceed the original prices which can be viewed as a risky customer approach by the brand. The variability observed in Images 6 and 7 above show price elasticities suggesting that this online media company should consider a price discounting strategy that targets a specific segment vs. all customers. Whether the company’s revenue will rise or fall depends on the price elasticity of product demand. Microeconomic theory predicts that lowering prices will increase revenue if demand is price-elastic (elasticity < –1) and decrease revenue if demand is price-inelastic (elasticity > –1). Image 9 below shows a report summary of SAS performing an evaluation of the revenue-maximizing policy s1, as well as six other discounting strategies for comparison with the observed pricing policy approach.
To be clear, as an analyst, the input training data for this exercise contains the observed number of products (songs) purchased at the observed prices. Thus, it is straight forward to compute the corresponding revenue per customer, but we wish to learn what the revenue per customer would be if the online media company had set a different price. For example, consider the different price strategies described in Image 9. The objective is to compare each of the hypothetical pricing policies with the observed prices to determine which strategy generates the most revenue. The SCORE and INFER statements within PROC DEEPPRICE enable analysts to do that after estimating the price effects and saving the details of the estimation (Image 10).
The results of this robust policy comparison are plotted in Image 11 below. Each of the hypothetical policies are compared to the observed policy price, and each blue-filled marker (some might see a resemblance to a Star Wars TIE Fighter) represents the monetary difference between each policy and estimated revenue per customer. The green vertical dotted line represents a revenue difference of zero (useful as a benchmark comparison). Markers on the left of the green line represent policies that are worse (revenue loss), and those on the right represent policies that are better than the observed strategy (revenue gain).
The optimal personalized revenue-maximizing policy, s1, mentioned earlier is clearly the best in the lower-right area of the graph. However, because prices under s1 can exceed the original prices, this can contradict how a brand desires to facilitate customer experiences. This is where human-driven marketing strategy and AI come together.
The discounted optimal personalized policy, s1d, is more meaningful as it performed a policy analysis with the constraint of never increasing the original product price for a customer. It resulted in an estimated net revenue improvement of $0.63 per customer. As a comparison, evaluated policies that offer every prospect a discount (s3 and s5) are worse than the observed approach (as they index below the baseline threshold).
PROC DEEPPRICE offers a unified framework for estimating heterogeneous causal effects of treatments and performing policy analysis. Its flexibility has enabled us to specify individual price effects as a function of individual customer characteristics. The estimated individual price effects were then translated into individualized prices. These were used, along with other hypothetical pricing strategies, to show how the online media company can set user-specific prices to achieve maximum revenue.
Now, let's activate on these insights to show how a brand can leverage the optimal discount policy to target a specific group of customers with the objective of increasing revenue.
SAS Customer Intelligence 360 - Take Us Away!
Brands today are complex ecosystems of decisions that must be executed with increasing levels of automation - due to their competitors digitally transforming and influencing customer expectations. In response, there is a need to change how decisions are made. Organizations have the opportunity to increase their capability to perform augmented decision making - where a human takes analytically driven insight to support decisioning (such as within promotional campaigns through outbound emails, or inbound 1:1 personalized interactions via website or mobile app). With each passing year, the acceleration of the scale, speed and complexity of customer 1:1 decisions is increasing.
SAS Customer Intelligence 360 is a martech solution to support adaptive planning, journey activation, personalization and AI-elevated decisioning to help users create appealing, moments-based customer experiences that boost profitability and strengthen brand loyalty. Within it, marketers are provided out-of-the-box (OOTB) connectors that are preconfigured for data integration with external applications (such as SAS Viya). Brands can use connectors to retrieve or transfer data between SAS Customer Intelligence 360 and on-premises or cloud-based applications. Our design intent with these connectors is to offer brands time-to-value acceleration in activating commonly used data management and analytical flows, such as the actionable output of sophisticated AI models that get created from procedures like PROC DEEPPRICE.
Now what would a software technology company like SAS with guerrilla-like data science powers as well as journey orchestration and marketing activation capabilities be up to here? Let's bring it home...
Here's the situation. The SAS Store is a retail brand with a digital presence.
Before proceeding, this website is instrumented with SAS Customer Intelligence 360 for digital interaction collection, contextualization, targeting and personalization. With that stated, the story line begins with customer engagement. An activity within SAS is a coordinated series of interaction tasks and events that are designed to meet the goals of a marketing objective. An activity map charts the customer paths between tasks, such as sending a particular message through a particular channel, and conditions, such as the primary measurement metric and evaluation periods. For example, users might use a variety of customer engagement tasks to raise awareness and present a product discount from their brand's website.
The tasks on the left side of Image 13 represent four mechanisms to qualify a prospective customer for this journey.
- Facebook (Meta) Ads task to target customers with a personalized pricing advertisement related to a product category
- Google Ads task to target customers with a personalized pricing advertisement related to a product category
- Web event monitoring for 1st party customer behavior that would qualify prospective interest in a product category
- Email task to target customers with a personalized pricing advertisement related to a product category
Each of these targeting tactics carry the same objective to provide a 1:1 model-driven pricing offer to entice the customer to interact with the SAS Store brand digitally and carry out further personalization to increase the efficiency of purchase conversion. Let's walk through the email task for transparency.
The use of merge tags and variables enables a marketer to personalize the email content in a 1:1 manner. Users can use merge tags to personalize the text of the creative for each recipient. When users enter a merge tag in a creative, the available merge tags are retrieved from uploaded customer data and are displayed consistently across tasks. For example, let's look up my Customer ID profile:
Image 15 highlights the fragmentation of identity management. Across a variety of cookie, browser, device and interaction keys, one master identity profile is deterministically linked with one customer. Digging deeper into the customer state service, we can observe available merge tags available for personalization. The property 'S1_Discount' is an example of preloaded 1:1 scoring extracted from the PROC DEEPPRICE exercise to produce the discounted optimal personalized policy. Recall, the 's1d' policy discussed earlier in this article resulted in the most meaningful outcome as it performed a policy analysis with the constraint of never increasing the original product price for a customer while maximizing estimated net revenue.
So, for my customer profile associated with the email address displayed in Image 16, users can perform responsive previews of the personalized email content to test the targeting results.
Tracking user interactions is one of the core features that enables a brand to respond in real time to users based on their profile, origin, browsing behavior, and so on. The interactions that are monitored are the driving force behind many of the features that SAS Customer Intelligence 360 offers. Assuming this, now I receive an email from the SAS Store brand.
As many of us can anticipate, I click-through on the email and I'm redirected to the brand's digital property. Based on the activity map highlighted above in Image 13, I will now receive a 1:1 personalized pricing treatment within the lower middle spot of the site's homepage.
Although I could go on with this use case, the transparency and value proposition of activating the AI-insights of PROC DEEPPRICE through SAS Customer Intelligence 360 has been demonstrated through screenshots. If readers prefer to view a demo, please enjoy the short video below:
For readers who have a desire to learn more, interactive demo videos for active users of SAS Customer Intelligence 360 are available here. Finally, go here to gain incremental awareness about how SAS can be applied for customer analytics, journey personalization, AI decisioning and integrated marketing.
