Analytics Tools for Marketing Teams

While Brand Tracks are important to measure the brand health of consumer brands, it might not always be possible for early-stage brands to commission brand tracks But you can use freely available data to measure the strength of different aspects of your brand. Here is how👇 A Brand Track typically provides 3 things: a) Brand Awareness Metrics: Top of mind, spontaneous & total awareness b) Brand Funnel: Awareness to consideration to purchase to preference c) Brand Imagery/Associations: Emotional & Functional associations of your brand a) Brand Awareness In a Brand Track, the respondent will be asked, “Which are the brands in the category you are aware of” The first brand to be named will be the Top-of-mind awareness. All the brands mentioned by the respondent will be spontaneous awareness. And once the respondents run out of brands, the researcher will name all brands in the category and ask them, “Have you heard of this brand”. If they answer Yes, it will be aided awareness. Total awareness = spontaneous awareness+ Aided awareness. The proxies to measure awareness are: 1. Brand Search Volumes on Amazon: One of the best updates Amazon has made in recent years is giving the exact weekly search volumes on Amazon. Go to Brand Analytics in your seller platform and you will find weekly/monthly/quarterly search volumes. The increase in search volumes is directly proportional to the increase in brand awareness( See image attached) 2. Brand Search Volumes/Clicks on Google: Search Console gives you the exact clicks and impressions data for the different brand search queries on a weekly/monthly/quarterly basis. Again, increase in brand search volumes is directly proportional to increase in awareness 3. Share of Brand Searches: While the previous 2 metrics gives you a sense of how your brand is doing, share of brand searches in a category gives you an idea of the relative strength of your brand awareness vis-à-vis competition. On Amazon Pi, you can get the category bifurcation of keywords into generic, competition and brand. Share of Search= Brand Search volumes/(Brand Search Volumes + Competition Search volumes ) If this number keeps increasing, relative strength of the brand awareness is going up For the complete Brand Funnel and Brand Imagery metrics, go through the link in the first comment In addition to Brand Awareness/Funnel/Imagery, I also recommend the following metrics to be tracked quarterly/half yearly/annually to track long term strength of a brand 1. Performance Marketing/BTL Spends as % Of Sales: Should keep reducing 2. Discounts on MOP as % of Sales: Should keep reducing 3. Trade Schemes as % of Sales: Should keep reducing 4. Annual Price Increase: Should be higher than inflation/category So, yes while Brand Tracks are important, there are enough free data points brands already have to help them track the short term & long term outputs of the brand marketing efforts Use them to the fullest

As consumers seek more individual experiences and interactions, companies turn to #AI to deliver 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙯𝙚𝙙 𝙥𝙧𝙤𝙢𝙤𝙩𝙞𝙤𝙣𝙨 𝙖𝙩 𝙨𝙘𝙖𝙡𝙚. For some time now, companies have been trying to address customer needs through #personalization, using data and analytics to craft more relevant consumer experiences. Using improved analytics models, brands and retailers can better provide valuable offers to micro-communities wherever they want to engage. Meanwhile, #genAI enables marketers to create tailored content that is relevant to those groups. According to McKinsey & Company, marketers should unlock personalization at scale, by upgrading five areas of their #martech stack and processes: 1. Data: by improving #data collection and analysis, marketers can gain deeper insights into customer behaviors and preferences. 2. Decisioning: to develop personalized promotions and content through more robust targeting, companies can also benefit from refreshing their #decision engines with new AI models. 3. Design: a sophisticated design layer that oversees offer management and #content production helps manage the process, fueling both operational excellence and agility. 4. Distribution: achieving true, real-time personalization requires a sophisticated #marketing architecture that delivers seamless and consistent messaging to the right audiences at the right time on the right channel. 5. Measurement: to validate the #ROI of personalization efforts, rigorous incrementality testing, standardized performance metrics, and measurement playbooks are essential. Are there other capabilities or technologies required for marketers to better target promotions and deliver individual content?

Amazon just rolled out a pretty cool update to Brand Metrics. Here's what you need to know: New features: -Category median benchmarks -Category top benchmarks -Percent change view Why it matters: 1. Compare your brand against category trends in real-time 2. Gauge if your growth is outpacing or lagging the category 3. Get instant insights without exporting data For example, say your beverage brand sees a 20% increase in shoppers. Sounds great, right? But what if the category median is up 25% and top performers are up 30%? This update helps you spot these crucial nuances instantly. The most useful tool is the percent change view. This feature will be huge for understanding your brand's performance in context. You can quickly see how you stack up during events like Prime Day, understand if a dip in numbers is brand-specific or category-wide, and measure the impact of your marketing efforts on awareness, consideration, and purchase metrics. My advice: Make the percent change view your first stop when analyzing performance changes. It'll help you differentiate between market trends and brand-specific issues, giving you the insights you need to make informed decisions.

For half a decade, I thought I was tracking the right metrics I was wrong Revenue. Growth rate. ROAS. Conversion rate. New customers. Repeat revenue All important But they could tell me the business was growing without telling me whether that growth was making the company more valuable You can buy more traffic, discount more aggressively, and acquire less-profitable customers while the top line keeps going up The business gets bigger That doesn’t automatically mean its equity value does A stronger Brand should make future revenue easier to earn, more profitable, and less dependent on buying every sale Here are the 11 metrics I wish I’d started tracking sooner, framed as questions: 1. Are branded organic searches growing faster than revenue? 2. Are contribution dollars and contribution margin going up? Contribution Dollars = Revenue - variable costs like COGS, marketing, and shipping 3. Is direct and branded search revenue growing faster than overall revenue? 4. Is the gap between gross and net sales shrinking? This signals less reliance on discounts and fewer returns 5. Are 30, 60, and 90-day incremental LTV going up, excluding the first purchase? 6. Is reach growing as fast as—or faster than—revenue? 7. Have your worst days gotten better? One way to measure this: is the average of your 30 lowest-revenue days trending up? 8. For organic search, is revenue per session rising while sessions are growing or stable? 9. Is your share of branded organic searches growing versus your competitive set—at both the Brand and category level? 10. Is Baseline Revenue growing, both in dollars and as a percentage of total revenue? I define Baseline Revenue as revenue from direct traffic, organic search, and organic social referrals It’s imperfect. But if it’s rising in dollars AND as a percentage of revenue, good things are generally happening 11. Is Baseline Revenue per branded organic search going up? Branded searches are an imperfect proxy for the Brand you’re building. Baseline Revenue per search shows whether you’re monetizing it better If searches are soaring but Baseline Revenue per search isn’t, that’s something to audit — A few caveats: None of these metrics are perfect. You can game any of them They’re also mostly leading indicators—not the ultimate company scorecard The ultimate outcome is more operating profit and net cash over time The right metrics also change with the company’s stage, economics, and strategy. A five-month-old company shouldn’t use the same scorecard as a 100-year-old company But if you can honestly answer “yes” to most of these questions, there’s a good chance the quality of your growth is improving And that gives you a better chance of building a more valuable company—not just a bigger one Question for the people of the internet: What else do you track to understand whether growth is increasing the quality and equity value of the business?

It's easy to fall into the "doing things just to do them" trap in demand gen and ABM.  👉🏾 Launching campaigns because "it's our typical approach." 👉🏾Creating content because "we have to."   👉🏾 Chasing every lead with the belief that "more is always better." But with AI and automation making it easier than ever to produce generic content, it's even more crucial to pause and ask, "Why?" ✔️Why this campaign? ✔️Why this content? ✔️Why this account? ✔️Does it truly align with our ideal customer profile (ICP)? ✔️Does it resonate with their needs and challenges? ✔️Does it get results on our goals? Generic #ABM is just...marketing. And generic #demandgen is a waste of resources. 👉🏾 To break the autopilot cycle, be specific about your ideal customer. Use tools like 6sense or ZoomInfo to gather rich data, going beyond basic demographics to understand their firmographics, technographics, and psychographics.  👉🏾 Then, map your content to the buyer's journey. Don't just create content for content's sake. Use tools like HubSpot or Marketo to address their pain points and provide real value at each stage. 👉🏾 Analyze intent data. Tools like Bombora or G2 Buyer Intent can tell you which accounts are actively researching solutions like yours, allowing you to focus your ABM efforts on those showing high intent. 👉🏾 Don't forget to make it a personalized experience. Use AI-powered platforms like Persado or Phrasee to tailor your messaging to individual accounts and show a deep understanding of their needs. 👉🏾 Finally, measure what matters. Track metrics that align with your goals, not just vanity metrics. Tools like Google Analytics or Bizible can help you measure the true impact of your ABM and demand gen efforts. 👉🏾 And most importantly, find someone to challenge your thinking. A colleague, a mentor, even a (kind!) competitor. Someone who asks: ✔️Why are we targeting this account? ✔️Will this content truly resonate? ✔️Does this campaign align with our overall strategy? Break free from autopilot, be intentional, and be strategic. Then, watch your ABM and demand generation results grow. What tools or strategies do you use to focus on the "why" behind your marketing? #b2bmarketing #marketingstrategy

In marketing, choosing the right campaign strategy — such as whether to reach customers through SMS or email — is critical. These decisions shape how effectively brands connect with their audiences. In a recent tech blog, Klaviyo’s data science team shared how they used uplift modeling and counterfactual learning to help marketers deliver more personalized campaigns at scale. The team began with a simple but powerful insight. Instead of defining audience segments first and then randomizing within each group to test different strategies, it’s mathematically equivalent to randomizing treatments first and segmenting afterward. In practice, this means you can run a single randomized experiment — for example, comparing SMS versus email — across the entire audience, and later analyze how different subgroups responded to each treatment. Building on this foundation, the team applied uplift modeling to estimate how each recipient would respond under different treatments. The result is a system that predicts which customers are more likely to engage via SMS versus email — and automatically personalizes campaign delivery accordingly. The team ultimately turned this approach into a product feature, empowering marketers to design smarter, data-driven strategies with minimal manual testing. It’s a great example of how causal inference and machine learning can go beyond analysis — directly shaping how real-world marketing decisions are made. #DataScience #MachineLearning #UpliftModeling #CounterfactualLearning #Personalization #Marketing – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBgBiTJj

E-commerce, especially on Amazon, is a competitive arena. To win, you need strategy and the right tools. That's where Amazon Marketing Cloud (AMC) comes in. It's more than just a data platform; it's a strategic advantage. Two ways AMC can help -> 1. Insights & Analysis AMC unlocks access to unique data sets, giving us an unprecedented view of the customer journey. We can finally get inside the shopper's mind – understanding how they discover, consider, and ultimately buy on Amazon. This translates to campaigns that are dialed in to real shopper intent. 2. Customised Audience on DSP and Search - AMC enables us to take those insights and make them actionable immediately. We can build highly specific audiences and activate them seamlessly within Amazon's DSP and, crucially, Sponsored Ads. Given the importance of Sponsored Ads for most Amazon strategies, this ability to layer AMC audiences is a major win for optimizing ad spend and maximizing ROI. Essentially, AMC allows brands to move beyond reactive tactics and adopt a proactive, data-driven approach to retail media. It's about truly understanding consumer behavior and using that knowledge to dominate the marketplace. Have you used AMC audiences on Search? #retailmedia #amazonads

How precise does your b2b marketing data get? Lately, I've been exploring technographic data – a tool that’s reshaping how I approach marketing personalization. Technographic data refers to information that describes a company's current technology stack, including the software, platforms, tools, and technologies they use to run their business. This data can reveal insights into a company's operations, technological capabilities, preferences, potential needs, and challenges. For instance, knowing a company uses WordPress not only tells us about their web platform choice, but can also indicate specific challenges they face, from security concerns to customization issues. Rather than a generic pitch, you can address their unique situation with a solution that fits perfectly. 👉 This level of personalized outreach doesn't just get attention; it positions you as an expert. You're already halfway through the door by demonstrating an understanding of their tools and challenges, shifting from a cold outreach to a meaningful conversation starter. #DigitalMarketing #TechnographicData #Personalization #MarketingStrategy

For years, companies have been leveraging artificial intelligence (AI) and machine learning to provide personalized customer experiences. One widespread use case is showing product recommendations based on previous data. But there's so much more potential in AI that we're just scratching the surface. One of the most important things for any company is anticipating each customer's needs and delivering predictive personalization. Understanding customer intent is critical to shaping predictive personalization strategies. This involves interpreting signals from customers’ current and past behaviors to infer what they are likely to need or do next, and then dynamically surfacing that through a platform of their choice. Here’s how: 1. Customer Journey Mapping: Understanding the various stages a customer goes through, from awareness to purchase and beyond. This helps in identifying key moments where personalization can have the most impact. This doesn't have to be an exercise on a whiteboard; in fact, I would counsel against that. Journey analytics software can get you there quickly and keep journeys "alive" in real time, changing dynamically as customer needs evolve. 2. Behavioral Analysis: Examining how customers interact with your brand, including what they click on, how long they spend on certain pages, and what they search for. You will need analytical resources here, and hopefully you have them on your team. If not, find them in your organization; my experience has been that they find this type of exercise interesting and will want to help. 3. Sentiment Analysis: Using natural language processing to understand customer sentiment expressed in feedback, reviews, social media, or even case notes. This provides insights into how customers feel about your brand or products. As in journey analytics, technology and analytical resources will be important here. 4. Predictive Analytics: Employing advanced analytics to forecast future customer behavior based on current data. This can involve machine learning models that evolve and improve over time. 5. Feedback Loops: Continuously incorporate customer signals (not just survey feedback) to refine and enhance personalization strategies. Set these up through your analytics team. Predictive personalization is not just about selling more; it’s about enhancing the customer experience by making interactions more relevant, timely, and personalized. This customer-led approach leads to increased revenue and reduced cost-to-serve. How is your organization thinking about personalization in 2024? DM me if you want to talk it through. #customerexperience #artificialintelligence #ai #personalization #technology #ceo

The Power of AI in Marketing 🌟  I’ve been reflecting on a pressing challenge we face: how to effectively engage an increasingly diverse and discerning customer base in a digital landscape flooded with noise. The Problem: Today’s consumers expect personalized experiences. However, with vast amounts of data and ever-changing preferences, it’s becoming increasingly difficult to deliver tailored marketing that resonates. Traditional methods often fall short, leading to missed opportunities and lower engagement. The Solution: Enter AI-powered marketing. Here’s how we can leverage AI and machine learning (ML) tools to transform our approach: 🔍 Personalization at Scale: AI tools like Segment and Dynamic Yield analyze customer data—demographics, purchase history, online behavior—to create hyper-personalized campaigns. Imagine sending the right message to the right person at the right time, leading to significantly boosted engagement and conversions! 📊 Data-Driven Decisions: Predictive analytics platforms like Google Analytics and Tableau enable us to forecast trends and understand customer preferences. With real-time sentiment analysis tools such as MonkeyLearn, we can adjust campaigns instantly based on what resonates most with our audience. ⚙️ Efficiency and Cost Reduction: AI streamlines our processes by automating repetitive tasks. Tools like HubSpot and Mailchimp can handle email marketing and reporting, freeing our teams to focus on creativity and strategy, enhancing productivity and reducing operational costs. 📈 Measurable Results: Robust analytics from platforms like Adobe Analytics and Kissmetrics provide insights into our campaigns’ effectiveness, allowing for continuous improvement. Imagine tweaking a campaign based on live feedback to maximize our ROI! 🚀 Competitive Advantage: Many competitors are already leveraging AI, making it crucial for us to stay ahead. By investing in AI-powered tools like Salesforce Einstein and IBM Watson, we position ourselves as innovators in our industry, enhancing our brand reputation. AI-powered marketing is not just a trend; it’s a strategic necessity. It helps us overcome the challenge of personalization, deepens customer relationships, enhances efficiency, and drives revenue growth. I’m excited about the possibilities and look forward to collaborating with our team on this journey! #AIPower #MarketingInnovation #DataDriven #Personalization #FutureOfMarketing #digitalmarketing