Stop Discounting Your Way to Growth.
Start Earning the Second Purchase.
Most retail and e-commerce marketing is a treadmill. Performance marketing acquires customers through discounts, paid search, and social advertising. Many of those customers came only for the promotion, not for the brand. When the promotion ends, they churn. To maintain revenue, the brand needs to acquire more customers to replace the ones that left. The acquisition cost goes up every year as competition intensifies and platform advertising costs rise. The margin goes down every time a discount is offered to acquire or retain a customer who would have left without one. This is not a growth strategy. It is a deficit that compounds.
Revenue marketing for retail and e-commerce breaks the treadmill by shifting investment toward the programs that generate the highest long-term revenue per customer: retention lifecycle marketing that earns the second, third, and fourth purchase from customers already acquired; loyalty and VIP programs built around genuine brand affinity rather than points accumulation; personalization that makes every customer interaction feel relevant rather than generic; and AI search visibility that captures shoppers at the earliest stage of product discovery before they reach a marketplace or a competitor's paid search result.
TPG's RM6 framework gives retail and e-commerce companies a maturity model and execution roadmap calibrated to the dynamics of consumer and B2B retail: perishable promotional windows, high customer acquisition costs, first-party data as competitive advantage, and the need to prove marketing's contribution to lifetime value and repeat purchase rate, not just first-purchase cost.
A retail brand generating $10M in annual revenue with a 30% discount rate on promoted purchases is spending $3M per year to generate demand it is training customers to expect. That $3M redirected toward behavioral lifecycle marketing, loyalty program development, and AI search visibility would generate higher return in year two because retained customers do not require discount triggers to repurchase. The brand that is not doing this math is funding its competitors' customer acquisition costs with its own margin erosion.
Retail & e-commerce segments TPG serves
Revenue Marketing Strategy for Retail & E-Commerce Companies
How to build a marketing strategy anchored in customer lifetime value rather than first-purchase cost, and sequence the program investments that compound over time.
How do retail and e-commerce companies build a revenue marketing strategy that breaks the discount acquisition treadmill and generates sustainable growth?
Retail and e-commerce companies build a sustainable revenue marketing strategy by diagnosing the specific point in the customer lifecycle where they are losing the most economic value and sequencing their program investment to fix that point first. Most retail and e-commerce brands lose the most value in the 30 to 90 day window after first purchase, when new customers who were acquired through paid search or social advertising receive no meaningful follow-up communications, have no connection to the brand beyond the transaction, and have no reason to return that is stronger than the next promotional offer they see from a competitor. This is where retention programs have the highest ROI because the cost of re-engaging a customer who bought once but has not yet lapsed is lower than the cost of acquiring a new customer and dramatically lower than the cost of winning back a customer who has already churned to a competitor.
TPG's RM6 diagnostic scores your retail and e-commerce marketing maturity across 49 capabilities and produces a prioritized roadmap that identifies the specific lifecycle stage where your program investment will generate the highest improvement in customer lifetime value, repeat purchase rate, and net revenue per cohort. For most retail companies, the highest-value first move is building a behavioral post-purchase sequence that converts first-time buyers into second-time buyers, because the second purchase is the strongest predictor of long-term brand loyalty.
Customer Acquisition & Demand Generation for Retail
How to acquire customers who are likely to become repeat buyers rather than one-time promotional purchasers, and measure acquisition cost against lifetime value, not just first-purchase revenue.
How do retail and e-commerce brands improve acquisition quality so that new customers are more likely to buy again without a promotional trigger?
Retail and e-commerce brands improve acquisition quality by targeting the specific audience segments whose behavioral profile most closely resembles existing high-lifetime-value customers rather than optimizing for the audiences that produce the lowest cost per first purchase. Cost-per-acquisition optimization selects for price sensitivity: the customers most responsive to promotional offers and the cheapest to acquire are typically the customers least likely to become loyal repeat buyers. Lookalike audiences and predictive acquisition targeting built on high-LTV customer profiles rather than all-customers profiles produce higher-quality acquisition cohorts at a modestly higher cost per first purchase that generates dramatically better revenue per cohort over a 12-month period.
The three channels that consistently produce the highest-quality acquisition cohorts for retail and e-commerce brands are owned content marketing that attracts customers through editorial value and product knowledge rather than promotional offers, referral programs that leverage the trust relationship between existing loyal customers and their networks, and search and social acquisition programs built on high-LTV customer audience seeds rather than broad demographic targeting. Each of these channels produces acquisition at a higher cost per first purchase than generic promotional advertising, and each produces cohorts with 40 to 60 percent higher lifetime value in the 12 months following acquisition. TPG builds retail acquisition programs that measure cost against lifetime value, not just first-purchase revenue, and sequence acquisition channel investment toward the sources that produce the highest long-term return per marketing dollar.
Customer Retention & Loyalty Marketing
How to build lifecycle programs that earn the second purchase, reduce churn before it happens, and build genuine loyalty that doesn't depend on being the lowest-price option.
How do retail and e-commerce brands build customer retention programs that generate repeat revenue without relying on discounts to trigger repurchase?
Retail and e-commerce brands build retention programs that generate repeat revenue without discounts by using behavioral data to deliver personalized communications that make the next purchase feel relevant rather than promotional. The distinction is important: a promotional email offers a discount to trigger a purchase from a customer who might not otherwise buy. A personalized behavioral email recommends a specific product based on what the customer has already bought, what similar customers have purchased next, or what complementary product would enhance the customer's use of their existing purchase. The personalized behavioral email converts without a discount because it is answering a question the customer already had, not creating artificial urgency through price reduction.
The three retention programs with the highest LTV impact for most retail and e-commerce brands are: a post-purchase onboarding sequence that activates new customers in the 7 to 30 days after their first purchase with product usage content, complementary product suggestions, and community connections that give them reasons to stay engaged with the brand beyond the transaction; a predictive cross-sell program that uses machine learning on purchase history data to recommend the next product each customer segment is most likely to want, timed to the optimal purchase probability window; and an early churn detection program that identifies customers whose engagement metrics are declining before they formally lapse, triggering personalized win-back programs before the customer has already mentally moved to a competitor. TPG builds these three programs for retail and e-commerce companies, connecting them to the CDP and marketing automation infrastructure that makes behavioral personalization possible at scale without proportional increases in marketing team headcount.
MarTech for Retail: CDP, CRM & Commerce Platforms
How to build a marketing technology stack that connects transaction data, behavioral signals, and customer identity into the unified profiles that personalization and attribution require.
How do retail and e-commerce companies build a MarTech stack that connects their commerce platform, CDP, and marketing automation without creating data silos between channels?
Retail and e-commerce MarTech works in four layers that most brands have not fully connected. The commerce platform layer handles the transactional experience: Shopify Plus and Salesforce Commerce Cloud for mid-to-large brands, Adobe Commerce for enterprise retailers with complex customization requirements. The customer data platform layer unifies customer identity and behavioral data from all channels into the single customer record that personalization requires: Segment (Twilio) and Salesforce Data Cloud are the most widely deployed CDPs for DTC and omnichannel brands. The marketing automation and CRM layer executes campaigns, lifecycle sequences, and behavioral triggers: Klaviyo is the dominant platform for DTC brands managing email and SMS; Salesforce Marketing Cloud serves enterprise multi-channel; HubSpot is common at mid-market retailers and B2B distributors. The analytics and attribution layer measures what programs drive revenue rather than what generates clicks.
The integration challenge that most retail marketing teams have not solved is the full data pipeline from commerce platform transaction to CDP customer profile to marketing automation behavioral score to attribution credit. Without this pipeline, behavioral personalization programs are built on incomplete data, lifecycle automation triggers on stale signals, and attribution credits first-click or last-click rather than the full multi-touch customer journey. TPG builds this integration architecture for retail and e-commerce companies across every combination of commerce platform, CDP, and marketing automation platform, starting with data governance agreements before technology configuration.
AI & Personalization for Retail Marketing
How to use AI for product recommendations, churn prediction, dynamic offer personalization, and AXO visibility that improves conversion and lifetime value without scaling headcount.
How do retail and e-commerce brands use AI to improve conversion rates and customer lifetime value at scale?
Retail and e-commerce brands use AI most effectively for five applications that target the specific revenue drivers of consumer and B2B retail. Product recommendation engines use collaborative filtering and individual behavioral data to surface the most relevant products for each customer at each touchpoint, increasing average order value on email, website, SMS, and post-purchase flows without requiring promotional discounts to drive the click. Predictive churn models analyze purchase frequency trends, engagement metrics, and recency signals to identify customers at elevated lapse risk before they formally disengage, triggering personalized win-back programs at the optimal intervention window rather than after the customer has already moved to a competitor. Dynamic offer optimization uses machine learning to match offer type and depth to individual customer segments, ensuring that high-margin customers who would buy at full price are not trained to wait for promotions while price-sensitive segments receive the offers that generate their best conversion rates. Demand forecasting uses historical purchase patterns to optimize inventory planning and reduce the margin-eroding clearance campaigns that result from imbalanced stock. And AXO structures product attribute data, customer review content, and brand story for AI search visibility, ensuring the brand appears when shoppers ask AI assistants for product recommendations before reaching any marketplace or search engine. TPG's R.A.I.N. framework applies all five AI capabilities to retail and e-commerce companies, integrating with CDP, commerce platform, and marketing automation data to generate predictions grounded in actual customer behavior.
Marketing Operations & Attribution for Retail
How to build the data infrastructure that connects acquisition programs to retention outcomes, channel investment to lifetime value, and marketing cost to net revenue per customer cohort.
How do retail and e-commerce companies build attribution models that measure marketing investment against customer lifetime value rather than last-click conversion?
Retail and e-commerce companies build attribution models that measure marketing against lifetime value by connecting the marketing analytics layer to the commerce platform and CDP in a data pipeline that retains source attribution data from the first acquisition touchpoint through every subsequent purchase in the customer relationship. Most retail attribution is broken in one of two ways: last-click attribution over-credits the promotional email or retargeting ad that drove the most recent purchase while under-crediting the brand content or referral program that originally acquired the customer; or first-click attribution over-credits the paid acquisition channel while under-crediting the retention programs that actually generate the majority of lifetime revenue. Neither model gives marketing and finance the information needed to allocate budget toward the programs with the highest lifetime ROI.
The attribution model that retail marketers need is a cohort lifetime value model: track each acquisition cohort from the channel and campaign that acquired them, measure their cumulative revenue at 30, 90, 180, and 365 days, disaggregated by acquisition channel, acquisition offer type, and product category. This model reveals which acquisition channels produce cohorts with the highest lifetime value and which retention programs extend the revenue curve of acquired cohorts most effectively. TPG builds retail marketing operations systems that produce cohort lifetime value reporting as the primary marketing performance metric, connecting commerce platform transaction data, CDP customer profiles, and marketing automation engagement data into the unified attribution infrastructure that makes this analysis possible.
B2B Retail & Distributor Marketing
How online distributors, B2B e-commerce platforms, and wholesale brands build demand generation programs that reach the actual purchasing decision-makers, not just the influencers.
How do B2B distributors and online retailers increase direct orders from procurement and purchasing decision-makers who are not being reached by existing marketing programs?
B2B distributors and online retailers increase direct orders from procurement decision-makers by building marketing programs that explicitly target the buyer persona with purchasing authority rather than defaulting to the technical or engineering audience that is easiest to reach through existing industry channels. The pattern is common across B2B distribution and wholesale: the company has strong relationships with technical influencers (engineers, specifiers, designers) who drive product selection but do not have purchasing authority, while procurement managers and purchasing directors who actually place orders have been overlooked because the company's content, channels, and sales enablement are built for technical audiences rather than commercial ones.
Revenue marketing for B2B distributors builds three specific programs for the procurement buyer persona: content marketing that addresses procurement decision-making criteria (total cost of ownership, supplier consolidation benefits, vendor compliance and certification, supply chain risk reduction) rather than technical product specifications; targeted demand generation through procurement-specific channels including trade publications, LinkedIn targeting by job function, and industry procurement associations; and a direct ordering proof-of-value program that demonstrates the specific economic benefits of ordering directly from the distributor versus through third-party intermediaries, including pricing transparency, inventory visibility, and account relationship management. Mouser Electronics used this architecture with TPG to achieve a 70 percent increase in orders from procurement professionals, a 35 percent shift from third-party distributor orders to direct purchases, and 10 percent overall revenue growth to $1.95 billion.
Content Strategy for Retail & E-Commerce Brands
How to produce content that answers the questions shoppers ask when evaluating products and brands, in the formats that earn AI citations, drive organic discovery, and convert browsers into loyal buyers.
How do retail and e-commerce brands create content that drives acquisition and retention without relying on promotional messaging to generate every engagement?
Retail and e-commerce brands create content that drives acquisition and retention without promotional messaging by building around the specific questions shoppers ask at each stage of their buying journey. Shoppers in the discovery stage ask questions about product categories, use cases, and whether a type of product will solve their specific problem. Shoppers in the evaluation stage ask questions about product comparisons, review quality, return policies, sustainability practices, and what distinguishes this brand from direct competitors. Shoppers in the repurchase and loyalty stage ask questions about new product releases, usage tips, community membership, and what makes the brand worth paying full price for rather than waiting for a sale. Content built around promotional messaging rather than these stage-specific shopper questions drives purchase only when a discount is attached, because it is not answering any question the shopper was actually asking.
Marketplace sellers face a distinct content challenge from DTC and omnichannel retailers. They are not trying to pull shoppers away from marketplaces. Amazon and Walmart are their primary discovery and transaction channels, and their content strategy problem is the inverse: how to build brand equity, direct channel awareness, and AI search visibility alongside marketplace presence so that loyal repeat customers eventually find and bookmark their direct site, reducing marketplace fee dependency over time. A marketplace seller whose brand story, product differentiation, and customer review content is structured for AI citation will appear when a shopper asks an AI assistant for the best sustainable home goods brand with strong customer service, even if the shopper originally discovered the brand through an Amazon search. That AI visibility creates a path to direct channel conversion that no Amazon listing can. Content that earns AI citations for brand attributes, not just product keywords, is how marketplace sellers build the direct relationship that reduces long-term platform dependency.
The additional urgency for all retail segments: shoppers are increasingly starting product discovery by asking AI assistants rather than typing into Google or browsing Amazon. When a shopper asks ChatGPT "what are the best sustainable running shoes under $150 with strong arch support and wide toe boxes," or asks Perplexity "which DTC mattress brands have the longest sleep trial and best customer reviews for side sleepers," the AI answers determine which brands enter the consideration set before any paid search, any social advertising, or any marketplace listing reaches the shopper. TPG builds retail and e-commerce content strategies that address human search behavior, AI search behavior, and marketplace-to-direct conversion, with AEO-structured content that earns citations in AI product recommendations and converts browsers across all channels into loyal repeat buyers.
AXO: AI Visibility for Retail & E-Commerce Brands
How to ensure your products and brand appear when shoppers ask AI assistants for recommendations, comparisons, and buying guidance before they reach any marketplace or paid search result.
How do retail and e-commerce brands get recommended by AI systems when shoppers research products and brands?
Retail and e-commerce brands appear in AI-generated product recommendations by structuring their product attribute data, customer review signals, brand story content, and buying guidance in the formats that AI systems can extract, trust, and cite when answering shopper queries. This is now the most important emerging visibility channel in retail and e-commerce, because the shopper behavior shift toward AI-first product research is happening faster and more comprehensively than most retail marketing teams have planned for. When a shopper asks ChatGPT "what are the best sustainable running shoes under $150 with strong arch support and wide toe boxes," asks Perplexity "which DTC mattress brands have the longest sleep trial and best customer reviews for side sleepers," asks Claude "which B2B electronic component distributors stock the largest inventory of microcontrollers and offer same-day shipping to the US Southeast," or asks an AI assistant "what are the best air fryers under $200 with easy cleanup and a large enough basket for a family of four," the AI answers determine which brands and products enter the consideration set before any Amazon search, Google shopping query, or social commerce feed reaches the shopper.
For retail brands with genuinely differentiated products, AXO is the channel that levels the playing field against larger competitors with bigger advertising budgets. AI systems do not rank by promotional spend. They rank by the specificity and verifiability of product attribute data, the quality and volume of customer review signals, and the depth of brand story content that can answer shopper questions at the attribute level. A mid-market DTC brand with a genuinely superior product specification and strong customer review data can appear in AI recommendations before a large retailer with generic product descriptions and less differentiated reviews. TPG's AXO Diagnostic scores your retail brand's current AI visibility across ChatGPT, Perplexity, Gemini, and Claude for the specific shopper queries that matter most to your product categories, identifies the content architecture gaps, and delivers a prioritized 90-day AEO roadmap for each product line and buyer persona.
Measuring Revenue Marketing ROI in Retail & E-Commerce
How to build attribution that measures cohort lifetime value by acquisition channel and proves the ROI of retention and loyalty programs that deliver revenue months after the initial investment.
How do retail and e-commerce companies prove revenue marketing ROI when the highest-return programs generate revenue months or years after the marketing investment?
Retail and e-commerce companies prove revenue marketing ROI by building cohort lifetime value models that trace the full revenue curve of each customer acquisition cohort from first purchase through 12 to 24 months of subsequent purchases, disaggregated by acquisition channel, campaign type, and product category. The challenge with traditional retail attribution is that it measures success at the moment of first purchase, which systematically undervalues retention programs that generate most of their revenue in months 3 to 12 and overvalues promotional acquisition programs that drive high first-purchase volume but produce low-LTV cohorts that churn at the first promotion from a competitor.
The six metrics that provide a complete retail revenue marketing ROI picture are: customer acquisition cost by channel disaggregated by predicted LTV tier, repeat purchase rate at 30, 90, and 180 days by acquisition cohort, average order value on second and subsequent purchases versus first purchase (the increase in AOV from product cross-sell and lifetime engagement), gross margin per customer at 12 months compared to acquisition channel cost, churn rate by acquisition cohort and retention program participation, and lifetime revenue per customer versus lifetime marketing cost per customer by segment and channel. Most retail and e-commerce brands have good first-purchase attribution data but poor visibility into which acquisition cohorts generate the highest 12-month revenue. Building the full cohort LTV model requires connecting commerce platform transaction data with marketing channel attribution data in a unified analytics environment. TPG builds retail marketing attribution systems that produce cohort lifetime value reporting by channel, enabling marketing budget allocation toward the programs with the highest long-term revenue per marketing dollar.
Retail & Commerce Companies That Turned Customer Data Into Revenue
"TPG was instrumental in helping us identify a target audience that could move the revenue needle quickly. They opened our eyes and helped us build the tools we needed to realize revenue."
Candice Willingham, Director Marketing Automation, Mouser Electronics
Read the full case study →"The Pedowitz Group helped us transform our approach to lead nurturing by implementing a structured, data-driven process. Their expertise in Marketo, buyer personas, and lead management allowed us to engage secondary market accounts more effectively."
Carolyn Mar, Senior Director Marketing, Four Seasons Hotels & Resorts
Read the full case study →"Instead of being seen as a traditional mailing company, Pitney Bowes was positioned as a cool, data-driven innovator, leading to record engagement, over 1,100 booth scans, and a post-event sales spike."
The Pedowitz Group Case Study, Pitney Bowes Brand Reinvention
Read the full case study →Revenue Marketing for Retail & E-Commerce: Common Questions
What is revenue marketing for retail and e-commerce companies?
Revenue marketing for retail and e-commerce companies is the discipline of connecting every marketing investment to measurable revenue outcomes: customer lifetime value, repeat purchase rate, average order value, retention rate, and net revenue per customer cohort. Most retail marketing optimizes for first-purchase acquisition cost through paid search, social advertising, and promotional offers, which creates a treadmill: acquire customers, many of whom came only for the discount, watch them churn, then acquire more to replace them.
Revenue marketing breaks the treadmill by building retention and loyalty programs that earn the second, third, and fourth purchase from customers already acquired, dramatically improving the economics of every acquisition dollar. The core shift is from optimizing for cost per first purchase to optimizing for revenue per customer lifetime. TPG's RM6 framework operationalizes this across six pillars calibrated to consumer and B2B retail dynamics.
How do retail brands reduce discount dependency and grow without promotional spend?
Retail brands reduce discount dependency by building behavioral personalization programs that deliver relevant product recommendations based on purchase history rather than promotional triggers, loyalty programs designed around genuine brand value exchange rather than points accumulation, and post-purchase engagement sequences that activate new customers into the brand community before they have the opportunity to lapse.
The economic argument is straightforward: a brand generating $10M annually with a 30% discount rate is spending $3M per year training customers to expect discounts. That $3M redirected toward behavioral lifecycle marketing, loyalty infrastructure, and AXO visibility generates higher return in year two because retained customers repurchase without discount triggers. TPG builds all three program types and connects them to cohort LTV reporting.
How do e-commerce companies improve customer lifetime value and repeat purchase rate?
E-commerce companies improve customer lifetime value through three high-impact programs: a post-purchase onboarding sequence in the 7 to 30 days after first purchase that activates new customers with product usage content and complementary product suggestions; a predictive cross-sell program that uses machine learning on purchase history to recommend the next product each customer segment is most likely to want at optimal timing; and an early churn detection program that identifies declining engagement before customers formally lapse and triggers personalized win-back programs at the optimal intervention window.
Each program requires a CDP that holds unified purchase history and behavioral signals accessible to email, SMS, and paid media channels. TPG builds these programs and the CDP integration architecture that makes behavioral personalization possible at scale.
What MarTech platforms do retail and e-commerce companies need for revenue marketing?
The retail MarTech stack involves four layers: the commerce platform (Shopify Plus or Salesforce Commerce Cloud for mid-to-large, Adobe Commerce for enterprise), the customer data platform (Segment or Salesforce Data Cloud for unified customer identity), the marketing automation layer (Klaviyo for DTC email and SMS, Salesforce Marketing Cloud for enterprise multi-channel, HubSpot for mid-market and B2B distributors), and analytics and attribution.
The critical integration is connecting commerce platform transaction data through the CDP to marketing automation behavioral scoring to attribution reporting. Most retail brands have strong point solutions but have not completed this full pipeline integration. TPG builds it across every platform combination, starting with data governance agreements before technology configuration.
How do retail and e-commerce brands use AI to improve marketing performance?
Retail and e-commerce brands use AI most effectively for product recommendation engines (increasing AOV without discounts), predictive churn models (identifying lapsing customers before they formally disengage), dynamic offer optimization (matching discount depth to individual customer segments so high-margin customers are not trained to wait for promotions), demand forecasting (reducing margin-eroding clearance campaigns), and AXO (appearing in AI product recommendations before shoppers reach any marketplace or search engine).
TPG's R.A.I.N. framework applies all five AI capabilities to retail companies, integrating with CDP, commerce platform, and marketing automation data to generate predictions from actual customer behavior rather than generic retail models.
How do B2B distributors increase direct orders from procurement decision-makers?
B2B distributors increase direct orders from procurement by building marketing programs explicitly for the buyer persona with purchasing authority, not just the technical influencer who specifies products. This requires procurement-specific content (total cost of ownership, supplier consolidation, compliance), targeted demand generation through procurement channels (trade publications, LinkedIn by job function), and a direct ordering proof-of-value program demonstrating the economic benefits over third-party intermediaries.
Mouser Electronics used this architecture with TPG to achieve 70% more orders from procurement professionals, a 35% shift from third-party distributor orders to direct purchases, and 10% overall revenue growth to $1.95 billion. The Mouser case is the clearest example in TPG's portfolio of revenue marketing breaking the distributor dependency pattern.
How do retail and e-commerce companies measure revenue marketing ROI?
Retail and e-commerce companies measure revenue marketing ROI through cohort lifetime value models: track the full revenue curve of each acquisition cohort from first purchase through 12 to 24 months, disaggregated by acquisition channel, campaign type, and product category. The six key metrics are customer acquisition cost by channel by LTV tier, repeat purchase rate at 30, 90, and 180 days by cohort, AOV on second and subsequent purchases, gross margin per customer at 12 months versus acquisition cost, churn rate by cohort and retention program participation, and lifetime revenue per customer versus lifetime marketing cost.
Most retail brands have strong first-purchase attribution but poor visibility into which cohorts generate the highest 12-month revenue. TPG builds the commerce platform to CDP to analytics pipeline that makes cohort LTV reporting possible, enabling budget allocation toward highest long-term revenue per marketing dollar.
What is AXO and why do retail and e-commerce brands need it?
AXO stands for AI Experience Optimization, and it is TPG's methodology for ensuring retail brands appear when shoppers ask AI assistants for product recommendations before reaching any marketplace or search engine. When a shopper asks ChatGPT which sustainable running shoes under $150 have strong arch support and wide toe boxes, or asks Perplexity which DTC mattress brands have the longest sleep trial for side sleepers, the AI answer determines which brands enter the consideration set before any paid search or marketplace listing.
For retail brands with differentiated products, AXO enables visibility based on product attribute quality and customer review data rather than advertising budget. A mid-market DTC brand with a genuinely superior product specification can appear in AI recommendations before a larger competitor with generic descriptions. TPG's AXO Diagnostic scores your visibility across four major AI platforms and delivers a 90-day roadmap by product line and buyer persona.
Stop Discounting Your Way to Growth. Start Earning the Second Purchase.
TPG has built revenue marketing systems for retail, e-commerce, and distributor companies that shift marketing investment from promotional acquisition toward lifetime value generation. Mouser Electronics: 70% more procurement orders, $1.95B in revenue. Four Seasons: persona-driven engagement with top planners. 19 years of practice. One guarantee: results or you don't pay.
Satisfaction guaranteed: redo or no charge.