Publications

The Challenge

You are about to launch a new product. You know the market is there. You have demand forecasts. But when someone asks, "What should we charge on day one?" — your model goes silent. That is precisely the problem with the Generalized Bass Model (GBM), the industry's go-to tool for new product forecasting. It can tell you how to adjust prices after launch, but only if you first assume a launch price. In effect, it asks you to guess the answer to the most important pricing question, and then helps you optimize everything else around that guess. If the guess is wrong, everything downstream is wrong too.

The Discovery

We built a utility-based diffusion model that eliminates the guesswork. It simultaneously determines both the optimal launch price and the optimal price trajectory for the entire product lifecycle. Tested on real adoption data across four durable goods categories — Color TV, Air Conditioners, Clothes Dryers, and Freezers — our model delivered consistently superior forecasting accuracy and pinpointed the exact timing of peak adoption in three out of four categories.

Here is where it gets alarming: GBM fails to detect statistically significant price effects in three of the four categories. This means companies relying on GBM may be operating under the illusion that pricing barely matters for adoption — when in reality, it is one of the most powerful levers they have.

The Surprise: Raise Prices After Launch

Every MBA pricing class teaches some version of "start low, then decrease." Our findings turn that on its head. Across all four product categories, the optimal price path is non-monotonic: prices should rise after launch, peak during the growth phase, and only then decline. Take Color TV: the optimal strategy launches at $580, climbs to $850 by period 9 as word-of-mouth accelerates demand, then gradually tapers to $745. The GBM instead recommends a steep nosedive from $610 to just $200 — a pricing policy that forfeits 13.8% of lifetime revenue.

The Playbook for Practitioners

Product managers: If you are using a Bass-type model for pricing decisions, not just forecasting, you are almost certainly leaving revenue on the table. GBM was built to predict diffusion curves, not to prescribe optimal prices. Treating its output as pricing guidance is a costly misuse of the tool.

Pricing teams: The optimization algorithm runs in seconds using standard R software. You can produce specific price recommendations by period, run sensitivity analyses across different elasticity assumptions, and recalibrate in real time as early sales data comes in. This is not a theoretical exercise — it is a deployable decision tool that extends naturally to competitive, multi-product settings.

Executives: Your price sensitivity estimate is the make-or-break input. Small differences in this parameter can shift the optimal launch price by a factor of seven. Before launch, invest in getting this number right — through conjoint analysis, A/B testing, or analogous product data. That investment will pay for itself many times over.

The Challenge

For four decades, behavioral economists have demonstrated a powerful phenomenon in the lab: place an inferior "decoy" product next to a target product, and consumers flock to the target. It is one of the most replicated findings in decision science. But here is the billion-dollar question that no one had answered: does it actually work in a real marketplace — with real money, thousands of alternatives, and consumers who are not sitting in a university classroom? Skeptics called it a laboratory artifact. We put it to the test.

The Discovery

We analyzed transaction data from more than 100,000 diamonds listed daily by a leading U.S. online jewelry retailer over an eight-month period. Our model disentangles two mechanisms that operate beneath the surface: detection (how often shoppers actually notice that one diamond dominates another) and boost (how dramatically that realization changes their buying behavior). The results surprised even us. Detection rates in the real world are strikingly low — just 11% to 25% depending on the price segment. But when a shopper does notice? The effect is explosive: dominant diamonds sell 1.8 to 3.2 times faster than comparable alternatives.

The Strategic Insight: Two Levers, Not One

The decoy effect is not binary — it operates through two independent levers that retailers can pull separately. Detection is shaped by how you display products: comparison tools, side-by-side layouts, and explicit price-anchoring all increase the odds that a shopper spots a dominance relationship. Boost is shaped by the magnitude of that dominance: the clearer and more dramatic the value gap, the stronger the pull toward the dominant product.

The profit impact varies strikingly by segment. In the low-price segment ($2K-$5K), budget-conscious shoppers search harder and detect dominance 25% of the time, driving a 25.4% profit increase. In the high-price segment ($10K-$20K), detection drops to just 11% — but the sales boost per detection is a massive 3.2x, generating a 13.9% profit increase. Different segments demand different playbooks.

The Playbook for Practitioners

E-commerce and retail leaders: Your product page design is a hidden pricing lever. Displaying comparable alternatives, surfacing savings relative to grade-level averages, and strategically structuring sort orders can all boost detection rates. Our simulations show that widening price dispersion by 50% within product grades yields an additional 0.5% profit — a meaningful lift at scale.

Assortment planners: Each additional decoy in the market contributes roughly 0.37% incremental profit. But the real opportunity is segment-specific positioning. In low-price segments, make comparisons easy and obvious. In high-price segments, ensure dominant products are discoverable within complex assortments.

Beyond diamonds: Any market where products can be ranked on multiple attributes — electronics, insurance plans, subscription tiers, used cars, SaaS pricing pages — is fertile ground for decoy strategies. This research provides the first empirical framework for quantifying your market's decoy profit contribution and engineering around it.

The Challenge

Companies entering emerging markets typically bring a familiar playbook: compete on price, optimize margins, and treat distribution as a back-office cost. This approach is dangerously wrong. Emerging markets are structurally different — infrastructure is patchy, retail networks are fragmented across radically different channel types, and consumers access products through pathways that bear little resemblance to developed-market supply chains. When firms run a standard pricing analysis that ignores these distribution realities, they get margin estimates that are wildly inflated. Our research using 48 months of data quantifies exactly how inflated: by as much as 55%.

The Discovery

We studied two competing insecticide manufacturers in India selling liquid and solid products through two fundamentally different retail channel types — paan-plus stores (small format, consumer-preferred, expensive to serve) and general stores (larger catchment, less preferred, cheaper to serve). We built a competitive model that jointly optimizes price and distribution decisions, and the results were eye-opening.

Distribution is not just a cost line — it is a demand-shaping weapon. More distribution in a channel increases demand by making products easier to find, but it simultaneously raises price sensitivity by giving consumers more options. This creates a strategic paradox that most firms completely overlook. When we strip distribution from the model — as virtually every standard margin analysis does — profit margins are overestimated by 7% to 55% depending on the brand, product form, and year. Any strategic decision built on those numbers is built on sand.

The Surprise: New Roads Do Not Trigger Price Wars

When governments invest in infrastructure — new roads, highways, better logistics — distribution costs fall. Economics 101 says this should trigger price competition and benefit consumers through lower prices. Our counterfactual simulations tell a completely different story: when distribution costs decline by up to 20%, neither firm meaningfully changes its prices. Instead, both firms pour the savings into distribution reallocation. The preferred brand expands aggressively into both channel types, leveraging its brand strength to capture newly accessible markets. The weaker brand retreats from channels where it cannot compete and doubles down where it has a fighting chance.

The implication is striking: in emerging markets, the real competitive battlefield is distribution coverage, not price. Consumers benefit not from price cuts, but from greater product availability. This is a fundamentally different competitive logic than what operates in developed economies.

The Playbook for Practitioners

Emerging market managers: Distribution intensity should be on equal footing with pricing in every strategic planning session. If your models optimize price while holding distribution fixed, you are making decisions based on phantom margins. Jointly optimize both across every channel and product form — or risk systematic overinvestment in markets that cannot deliver the returns your spreadsheet promises.

Market entry teams: Standard due diligence will overstate the profit potential of emerging markets. Build the true cost of channel-by-channel distribution into your entry analysis. Your competitive position will be defined as much by where and how you distribute as by what you charge.

Strong brands: Infrastructure improvement is your signal to expand. When distribution costs fall, push into every channel — your brand preference advantage generates positive returns even where you are less favored. Weaker brands should do the opposite: consolidate distribution into channels where you can win, and avoid spreading resources across terrain you cannot hold.

The Challenge

Companies invest billions to build brand loyalty — through product quality, advertising, habit formation, and retention programs. The assumption is that loyalty generates profits for the brand that built it. But this research reveals an uncomfortable truth: in a distribution channel, the retailer captures the lion's share of the value that manufacturers invest to create. The question every brand manager should be asking — "Who actually profits from the loyalty I am building?" — has an answer most will not like.

The Discovery

Using scanner data from the cola market, we built the first dynamic pricing model that captures how both manufacturers and the retailer strategically respond to consumer switching costs. Prior research assumed manufacturers sell directly to consumers, as if the retailer does not exist. This omission is not a harmless simplification — it fundamentally distorts the picture of who wins and who loses when consumers are loyal.

The core tension: manufacturers face a classic invest-or-harvest tradeoff. They can cut wholesale prices today to attract new customers who will become loyal tomorrow ("invest"), or charge premium prices to extract margin from their already locked-in base ("harvest"). But the retailer faces no such dilemma. It carries both brands. Brand-specific loyalty does not shift the retailer's customer base — it only tells the retailer which brand it can mark up. So the retailer simply harvests. Always. It raises retail margins on whichever brand a customer is loyal to, capturing the value that the manufacturer paid to create.

The Free-Rider Trap

The mechanism is simple, but its consequences are devastating for manufacturers. They cut wholesale prices to lock in customers. The retailer then raises retail prices on those same locked-in customers, pocketing the margin. The retailer's harvesting incentive drives the majority of retail price increases, while its investing incentive is effectively zero — it will never sacrifice margin to help a manufacturer build future demand. This is the vertical conflict at the heart of loyalty economics.

The less preferred brand in our data (Coke) faces an especially punishing position. It must invest harder — cutting wholesale prices more aggressively — to compete for customers against the preferred brand (Pepsi). This compresses Coke's margins by 22-23%, compared to about 17% for Pepsi. The more preferred brand, by contrast, can lean more on harvesting. Brand preference and the invest-harvest dynamic create a compounding advantage for market leaders.

The Playbook for Practitioners

Brand managers: Before pouring investment into switching-cost programs — subscriptions, loyalty rewards, habit-forming product design — model how much of that value your retail partners will capture through margin expansion. Your loyalty investment may be subsidizing the retailer's bottom line more than your own. The strategic payoff of loyalty depends critically on how the retailer passes through (or absorbs) your wholesale pricing decisions.

Retailers: Your ability to harvest loyalty-driven margins is powerful but fragile. It erodes as consumers become more price-sensitive and as comparison shopping becomes easier. Protect your highest-margin categories by investing in category management practices that maintain healthy switching costs — because when inertia disappears, so does your margin cushion.

Industry strategists: Any analysis of loyalty programs, switching costs, or competitive dynamics that ignores the retailer's strategic pricing response is missing most of the picture. Our model shows that the conventional manufacturer-only framework underestimates total channel margins by 2.7 times. The retailer is not a passive conduit — it is a strategic player that reshapes the entire economics of brand loyalty.

The Challenge

Email is among the highest-ROI marketing channels in existence. Yet most companies manage it with a shockingly blunt tool: blast the same volume to everyone and watch the open rates. The governing assumption — that customers who open more emails are your most valuable customers — feels intuitive. It is also wrong. And the cost of acting on it is staggering: up to a third of customer lifetime profit, gone.

The Discovery

Using 39 months of granular transaction and email engagement data from a U.S. home improvement retailer, we uncovered three hidden customer states that silently govern both email behavior and purchasing. They defy the conventional segmentation logic:

The Silent Buyers (State 1): They barely glance at your emails (0.2 opens/month) — but they are quietly purchasing at a steady clip (0.34 purchases/month). Your email dashboard flags them as disengaged. Your P&L says otherwise.
The Engaged Window-Shoppers (State 2): They open nearly every email you send (2.7 opens/month) but almost never buy (0.15 purchases/month). They look like gold on your engagement reports. They are not.
The True VIPs (State 3): Highly engaged and actively buying (2.8 opens, 1.2 purchases/month). They are your most valuable segment — and also the most fragile. Push them too hard, and they break.

The Counterintuitive Prescription

Here is what makes email strategy so treacherous: your best customers are the most vulnerable to over-emailing. When True VIPs (State 3) receive too many emails, their probability of staying in the high-purchase state plummets from 47% to just 26%. You are literally pushing your best customers off a cliff with your send button. Meanwhile, the Silent Buyers (State 1) — the ones your system writes off as unresponsive — actually benefit from more emails: sending 10 per month lifts their probability of transitioning to a more active purchasing state from 5% to 21%.

The ironic prescription: send fewer emails to the customers who open them, and more emails to the customers who seem to ignore them.

The Playbook for Practitioners

Email marketing managers: Open rates are a vanity metric. They measure engagement, not value. A customer who opens every email but never buys is a phantom lead. Build segmentation that jointly tracks email engagement and purchase behavior. Allocate email frequency based on a customer's inferred purchasing state — not how often they click.

CRM teams: Static segmentation is not enough. The same customer needs different email frequencies at different points in their lifecycle. Our decision support system shows that one customer's optimal contact can swing from 5 to 14 emails per month as their behavioral signals evolve. Dynamic, state-dependent policies that recalibrate monthly based on purchase recency and email engagement will dramatically outperform fixed-cadence strategies.

Executives: Email profitability is non-linear. Doubling your send volume will not double your returns — it may shrink them. The gap between a naive email policy and an optimized one is measured in tens of percentage points of customer lifetime value. That makes email frequency optimization one of the highest-leverage investments in your marketing stack.

The Challenge

Every retailer sits on a goldmine of customer purchase data. Every manufacturer wants access to it — to personalize offers, target competitors' loyal customers, and win the coupon war. The conventional logic is straightforward: better data means better targeting means more profit. But what happens when you combine personalized pricing with brand loyalty in a competitive distribution channel? The answer, as our research reveals, is a power shift so dramatic that it redefines who wins and who loses in the entire value chain.

The Discovery

Using data from the cola market, we modeled how retailers and manufacturers can deploy behavioral price discrimination (BPD) through targeted coupons in a market with switching costs. The critical insight: a customer's price sensitivity is not fixed — it changes based on what they bought last time. A consumer who just purchased Pepsi becomes less price-sensitive to Pepsi and more responsive to a Coke discount. This time-varying elasticity, driven by switching costs, creates a targeting opportunity that is far richer than standard personalization models assume.

We compared three strategic arrangements: the retailer managing couponing itself, the retailer outsourcing couponing to manufacturers, and the retailer selling its customer database as an information broker. The results upend the conventional wisdom about who benefits from data-driven personalization.

The Data Paradox: More Information, Less Profit

Here is the central paradox that should keep every CPG manufacturer up at night: manufacturers willingly pay for access to customer data, yet they end up earning less than they would in a world with no personalization at all. How? When both competing manufacturers gain targeting data, they simultaneously escalate efforts to poach each other's loyal customers. The result is a coupon arms race that shreds manufacturer margins while the retailer collects fees for fueling the fire.

Customer data, in a channel with switching costs, is not just a marketing tool. It is a structural source of channel power. The retailer's advantage comes not from smarter analytics, but from the fact that sharing data with competing brands triggers a competitive spiral that systematically transfers value from manufacturers to the retailer. Data is the new shelf space.

The Playbook for Practitioners

Retailers: Your customer data is worth far more as a strategic asset than as a direct marketing input. The winning strategy: outsource coupon design and delivery to manufacturers while retaining data ownership — and charge for access. Acting as an information broker generates up to 27.8% more profit than running BPD programs yourself. You are not a distribution partner. You are a data broker with a storefront.

Manufacturers: Tread carefully when negotiating for retailer data. Yes, it enables better targeting — but it also hands your competitor the same weapon and ignites a margin-destroying arms race. The terms of data access agreements — exclusivity, pricing, scope — are as strategically important as the data itself. In some competitive scenarios, the rational move is to walk away from the data entirely rather than participate in a value-destructive escalation.

Anyone starting with personalization: Begin with last-purchase targeting. One data point — what the customer just bought — is enough to power a meaningful BPD program. It costs almost nothing to implement (print a targeted coupon on the receipt) and captures substantial value. Do not build the data warehouse until you have exhausted the power of this single signal.

The Challenge

Manufacturers in emerging markets face a problem they often misdiagnose. Their intermediaries — dealers, wholesalers, institutional buyers — are underperforming, and the reflex response is to throw incentives at them: cash discounts, volume bonuses, sponsored trips. But the real problem is not motivation. It is misalignment. Intermediaries are carrying the wrong products in the wrong markets. No amount of incentive can compensate for a structural mismatch between what an intermediary sells and what its local market actually wants to buy.

The Discovery

We ran three interconnected studies to crack this problem. First, we interviewed 24 managers and 29 intermediaries across four industries (construction, FMCG, heavy machinery, automobiles). The message was clear from both sides: manufacturers push products without understanding local demand. As one dealer put it, "The company should know what the market is looking for." The concept of distribution alignment — systematically matching the right intermediary type with the right product in the right market — emerged as the central opportunity.

Second, we analyzed 37 months of sales data from an Indian construction product manufacturer. The empirical model revealed dramatic performance differences across the 12 possible combinations of intermediary type (dealers vs. institutional buyers), market type (rural vs. semi-urban vs. urban), and product type (premium vs. non-premium). The gaps are not marginal — they are large enough to power a complete strategic reallocation.

Third — and this is what sets this research apart — we took the recommendations into the field. Over six months, one region received data-driven guidance on which products to push through which intermediary types in which markets. A matched control region received business as usual.

The Core Insight: Alignment Creates Shared Value

This is not a zero-sum reallocation. When intermediaries focus on products that genuinely sell in their local markets, they move more inventory, earn better returns, and become more satisfied with the manufacturer relationship. The manufacturer gains because distribution resources flow to their highest-return combinations. Both sides win — not because of bigger bonuses, but because of better fit.

The approach requires no new incentives, no additional marketing spend, and no expanded headcount. It requires only better information and the willingness to guide intermediaries toward more productive configurations. One automobile dealer in our qualitative study who received alignment advice sold 23% more cars in a single month — simply by focusing on what his market actually wanted instead of carrying the manufacturer's full product line indiscriminately.

The Playbook for Practitioners

Distribution managers: Audit your intermediary-product-market allocation today. Ask a simple question: are you distributing based on data, or based on habit? Not every intermediary should carry every product in every market. A data-driven alignment strategy — guiding each intermediary toward products that match their market — delivered double-digit profit improvements in our field test within just six months.

Sales force leaders: Your field team is the delivery mechanism for alignment. Stop pushing uniform volume targets. Instead, equip your reps with market-specific intelligence: which products to emphasize, which intermediary types to prioritize, and what local demand patterns reveal. This transforms the sales conversation from arm-twisting to partnership — and the numbers show it works.

Emerging market strategists: This approach is especially powerful where intermediary networks are fragmented, infrastructure is uneven, and market heterogeneity is extreme. The gap between current practice (uniform distribution) and optimal practice (aligned distribution) is widest in exactly these environments. And it requires no technology platform or capital investment — just the willingness to look at your data and act on it.