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  • News Title 05 | bauer/DIGITAL

    News Title 05 News Title 05 2/28/23, 10:00 PM This is a paragraph. It is connected to a CMS collection through a dataset. Click “Edit Text” to update content from the connected collection. This is a paragraph. It is connected to a CMS collection through a dataset. Click “Edit Text” to update content from the connected collection. Previous Next

  • B2B Dominate Niche Market | bauer/DIGITAL

    Helping a B2B Company Dominate a Niche Market USA Corporate has deep expertise in business incorporations, they strategically pivoted to specialize in serving non-resident clients. To align their digital presence with this strategic shift, they partnered with our agency. IMPACT Strategic Shift: From general incorporation services to helping non-residents navigate the US process. Together, we developed an integrated strategy to establish USA Corporate as the go-to resource for non-resident incorporations. Optimizing the website for the global audience, we implemented educational content, industry-leading SEO, and marketing automation. This automated lead nurturing and sales handoffs, enabling 24/7 pipeline generation. ​ By providing value to audiences across time zones, the revamped digital approach conveyed deep specialty knowledge. The always-on systems nurtured leads even outside business hours. Sales could focus on qualified, sales-ready prospects. Impact 10x increase in website traffic 5x growth in leads from organic search 60% of leads now generated from automated campaigns Improved sales productivity and shortened sales cycles The digital strategy and execution cemented USA Corporate's position as the top niche incorporation specialist. Automation fueled their rapid growth and pipeline velocity. 8 Digital Marketing Trends for 2024 a glimpse into the future, where technology, inclusivity, and sustainability play important roles in shaping marketing strategies. The Rise of Qualified Clicks: How AI is Transforming Search to Reward Relevance With the rise of AI, success is now defined by the quality of clicks rather than quantity of keywords - the new SEO battleground. Unlocking Email Marketing's Potential: Some Compelling Stats Brands Should Know Email marketing remains one of the most efficient channels for engaging customers and driving conversions. LinkedIn Algorithm Changed: How to Maximize Your Reach LinkedIn, the leading professional networking platform, recently made significant updates to its algorithm.

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Blog Posts (63)

  • LinkedIn's New CTV and Live Event Ads: Precision Targeting for B2B Marketers

    LinkedIn has just announced the launch of two exciting new ad solutions - LinkedIn CTV Ads and Live Event Ads - aimed at helping B2B marketers reach and engage key decision makers with precision targeting [1]. With LinkedIn CTV Ads, marketers can now expand their campaign reach beyond the LinkedIn platform and capture buyer attention across a network of premium streaming content providers. This includes access to NBCUniversal's premium inventory through LinkedIn Premiere, a new managed offering. By leveraging Kantar's deep audience insights, marketers can measure the impact of their CTV campaigns on brand awareness, affinity, and resonance among their target B2B audience. In addition to CTV Ads, LinkedIn is also introducing Live Event Ads to help marketers drive registrations and engagement for their live events. This new ad format allows marketers to promote their events to a highly targeted audience of professionals who are most likely to attend and engage. The launch of these new ad solutions comes at a time when B2B marketers are facing increasing challenges in reaching and engaging their target audience. With only 5% of buyers in-market for a new purchase at any given time, consistent engagement across multiple channels is key to staying top-of-mind and memorable when it's time to buy. At bauer/DIGITAL, we're excited about the potential of LinkedIn's new CTV and Live Event Ads to help our B2B clients achieve their marketing goals. By combining LinkedIn's robust targeting capabilities with the power of CTV and live events, we can create highly personalized and engaging campaigns that reach the right decision makers at the right time. Whether you're looking to build brand awareness, drive event registrations, or generate high-quality leads, LinkedIn's new ad solutions offer a compelling way to stand out and make an impact with your target audience. As an early adopter, you can gain a competitive edge and achieve better results from your advertising spend. If you're interested in learning more about how bauer/DIGITAL can help you leverage LinkedIn's new CTV and Live Event Ads to achieve your B2B marketing goals, contact us today. Our team of experts is ready to help you craft a winning strategy that delivers measurable results.

  • Attribution and Dynamics in Online Advertising

    Table of Content General Questions on Attribution and Dynamics in Online Advertising Do display ads influence search ad performance and conversions? Do online ads exhibit dynamic effects that improve effectiveness over time? How should attribution and dynamics impact online advertising metrics and budget allocation? Harvard Study on Search and Display Ad Interaction Nielsen Study on New Media's Brand Impact Lumen/Teads/Dynata Study on Attention and Brand Effects Harbine Engineering Journal Study on Ad Impact Columbia Business School Meta-Analysis Summary of key findings General Questions on Attribution and Dynamics in Online Advertising Online advertising has become a pivotal component of marketing strategies across industries. As businesses increasingly allocate substantial budgets towards digital channels, a pressing need arises to comprehend the intricate dynamics and attribution effects that shape the success of these campaigns. This post delves into the fundamental questions that underscore the importance of understanding attribution and dynamics in online advertising: A. Do Display Ads Influence Search Ad Performance and Conversions? One of the core inquiries revolves around the interplay between display advertising and search advertising. Specifically, marketers seek to unravel the extent to which display ads influence the performance of search campaigns, including: Search ad clicks: Do display ad exposures drive consumers to click on search ads, thereby increasing click-through rates (CTRs) and potential conversions? Search ad conversions: Beyond clicks, do display ads contribute to actual conversions from search campaigns, such as purchases, lead generations, or other desired actions? Search funnel progression: How do display ads impact the various stages of the consumer journey, from initial awareness to active search and eventual conversion? Answering these questions is crucial for optimizing cross-channel marketing strategies and accurately attributing credit to each touchpoint along the customer's path to conversion. B. Do Online Ads Exhibit Dynamic Effects that Improve Effectiveness Over Time? Another critical area of investigation focuses on the dynamic effects of online advertising. Specifically: Carryover effects: Do the impacts of online ads persist and accumulate over time, even after the initial ad exposure? If so, how long do these carryover effects last? Wear-in effects: Is there a lag between ad exposure and its impact on consumer behavior, where the effects "wear in" gradually over time? Long-term effectiveness: How do the long-term effects of online ads compare to their immediate or short-term impacts? Are there significant differences in effectiveness over various time horizons? Understanding these dynamic patterns is essential for accurately measuring the true return on investment (ROI) of online advertising campaigns and optimizing ad timing and frequency. C. How Should Attribution and Dynamics Impact Online Advertising Metrics and Budget Allocation? The insights gained from studying attribution and dynamics have profound implications for how marketers evaluate and optimize their online advertising efforts, including: Failure to account for attribution and dynamics can lead to: Inaccurate performance measurement and ROI calculations Suboptimal budget allocation across channels and campaigns Missed opportunities to capitalize on synergies and compounding effects Premature termination of effective campaigns due to underestimated long-term impact As businesses strive to maximize the impact of their online advertising investments, addressing these fundamental questions becomes paramount. By gaining a comprehensive understanding of attribution and dynamics, marketers can unlock valuable insights, refine their strategies, and ultimately drive superior returns on their digital marketing efforts. Harvard Study on Search and Display Ad Interaction The Harvard study by Kireyev, Pauwels, and Gupta (2013) provides valuable insights into the interaction between paid search and display advertising in driving consumer conversions. The researchers developed a multivariate time series model using data from a large commercial bank on its online marketing spend, ad impressions/clicks, and customer acquisitions over a 1-year period. Methodology The study employed a comprehensive persistence modeling approach to capture the complex dynamics and interdependencies in online advertising. Specifically: Granger causality tests identified which variables should be treated as endogenous in the model. Unit root tests determined which variables exhibited non-stationary behavior and should enter in differences. Cointegration tests uncovered stationary linear combinations representing long-run equilibrium relationships. Based on these tests, the researchers specified a vector error correction (VEC) model with all variables as endogenous, allowing for rich interactions. Key Findings of the Harvard Study on Search and Display Ad Interaction The VEC model enabled the authors to derive several important findings through impulse response analysis: 1. Display Ads Increase Search Conversions A key finding was that display ad impressions significantly increased paid search conversions, but this effect was not immediate. It took around 2 weeks for display ads to start positively impacting search ad conversion rates. This highlights the importance of accounting for cross-channel attribution effects rather than crediting only the last click. 2. Strong Dynamics and Carryover Effects Both search and display ads exhibited strong positive carryover effects that improved their effectiveness and return on investment over time. Ignoring these dynamic effects can lead to undervaluing the long-term impact of online ads. 3. Display Increases Search Costs In addition to increasing search conversions, greater display ad exposure also drove more search ad clicks and costs. So the overall impact of display needs to account for this added search spend. 4. Revised Performance Metrics After accounting for attribution to search, carryover effects, and added search costs, the study found: Each $1 spent on display ads yielded $1.24 in revenue Each $1 on search ads yielded $1.75 in revenue This contrasted sharply with estimates based on standard last-click attribution metrics used by the bank. 5. Optimal Budget Reallocation The revised performance metrics had major implications for optimal budget allocation between search and display: Despite display's attribution benefit, the strong dynamic effects for search called for increasing the search budget share by up to 36%. The display budget share should be reduced by 31%. This reallocation was driven by the much higher advertising elasticities for search after properly accounting for dynamics and cross-channel effects. The authors conducted several robustness checks on their modeling approach and results: Estimating a basic VAR model showed qualitatively similar short-term and wear-in effects, confirming the non-stationarity only impacted long-run behavior. Variance decompositions confirmed display impressions significantly drove search impressions (40%), clicks (17%), and conversions (16%). Using different shock identification schemes did not materially impact the impulse response estimates. The rigorous econometric methodology, extensive robustness analysis, and use of granular marketing data from a major firm lends strong credibility to the study's findings. By quantifying the complex interplay between search and display ads, the study provides a framework for resolving attribution problems and optimizing budget allocation across digital channels based on their long-term effectiveness. Nielsen Study on New Media's Brand Impact The Nielsen study aimed to quantify the impact of emerging digital media formats like podcasts, influencer marketing, and branded content on key marketing metrics such as brand awareness, brand recall, and return on investment (ROI). The large-scale study analyzed over 1,000 campaigns across these new media channels spanning multiple industries and brands. Groups were surveyed before and after the campaign to measure changes in key brand metrics like: Unaided Brand Awareness: Percentage who mentioned the brand when asked an open-ended question about brands in the category. Aided Brand Awareness: Percentage correctly identifying the brand when shown visual logos/assets. Brand Favorability: Percentage who rated the brand favorably on a scale. Purchase Intent: Percentage likely to purchase the brand's products. Key Findings of the Nielsen Study on New Media's Brand Impact The study yielded several important insights regarding the ability of new media formats to drive upper-funnel brand marketing objectives: 1. Significant Brand Awareness Impact Across the 1,000+ campaigns analyzed, Nielsen found that the median brand awareness lift among consumers exposed to podcasts, influencers, or branded content was over 70% higher compared to the control group. This highlights the powerful ability of these emerging channels to increase brand salience and top-of-mind recall, even for consumers not actively seeking out brand information. 2. Variance in Effectiveness While the median brand awareness lift was 70%, Nielsen observed significant variance in effectiveness across campaigns. The top quartile of campaigns achieved awareness lifts over 150%, while the bottom quartile saw negligible or even negative lifts. This variance was driven by factors like: Content quality and creativity Audience targeting and relevance Integration with other media channels Clarity of brand messaging and presence 3. Impact on Lower-Funnel Metrics In addition to brand awareness, the study also analyzed the impact on metrics lower in the marketing funnel: While not as pronounced as brand awareness, Nielsen found meaningful lifts in brand perception, purchase intent, and short-term return on ad spend from new media campaigns. This suggests that these channels can effectively drive outcomes beyond just awareness when executed properly and complemented with mid/lower-funnel tactics. 4. Importance of Attention and Viewability A key driver of effectiveness was the ability to capture consumer attention and ensure the brand messaging was viewable. Campaigns with higher viewability rates and longer average attention times tended to see larger lifts across all brand metrics analyzed. This underscores the importance of using new media formats and distribution channels that foster active engagement rather than just passive exposure. The Nielsen study provides robust evidence that digital platforms like podcasts, influencers, and branded content can be powerful vehicles for building brand awareness and driving marketing impact when leveraged strategically. By using an advanced methodology to isolate the true effects of new media exposure, the study highlights both the significant potential of these channels as well as the key factors that differentiate high and low performing campaigns. As consumer attention becomes increasingly fragmented across digital media, these insights can help brands navigate the evolving landscape and optimize their media mix for maximum brand building impact. Lumen/Teads/Dynata Study on Attention and Brand Effects This study was a collaborative effort between Lumen Research, Teads, and Dynata, aimed at quantifying the relationship between attention metrics for digital advertising and the resulting brand effects. It involved a large-scale meta-analysis spanning campaigns across 14 major advertisers in 2022 and 2023.The analysis combined two key data sources: Third-Party Attention Data Collected by Lumen across over 500 digital ad campaigns Measured viewable impressions and attentive seconds per impression Covered various ad formats like display, video, mobile, etc. Brand Lift Studies Conducted by Dynata for the same set of campaigns Surveyed a treatment group exposed to the ads and a control group Measured impact on brand awareness, favorability, consideration, purchase intent By combining these robust datasets, the researchers could analyze the correlation between objective attention metrics and the resulting brand KPIs at a very granular level. Key Findings of the Lumen/Teads/Dynata Study on Attention and Brand Effects The meta-analysis uncovered several important insights regarding the relationship between attention and brand effects: 1. Clear Correlation Between Attention and Brand Metrics Across the 500+ campaigns studied, the analysis found a statistically significant and "clear correlation" between attentive ad exposure and positive shifts in upper-funnel brand metrics like: Unaided Brand Awareness Aided Brand Awareness Brand Favorability Brand Consideration The higher the attentive seconds per impression, the greater the observed brand lift tended to be. 2. Threshold Effects for Lower-Funnel Metrics While attention correlated with upper-funnel metrics across the board, the study found evidence of threshold effects for lower-funnel metrics like purchase intent: Minimal lift was seen at low attention levels (<5 attentive seconds) A steep increase in purchase intent lift occurred between 5-10 attentive seconds Beyond 10 attentive seconds, further lift diminished This suggests that driving lower-funnel KPIs requires a higher bar for attentive ad exposure compared to just building awareness. 3. Outperformance of Attentive Impressions The analysis compared the brand impact of "attentive impressions" (defined as >5 attentive seconds) versus standard viewable impressions: Attentive impressions consistently drove 2-4x higher brand lift across the board compared to just viewable impressions. 4. Creative Optimization Opportunities While overall attention levels were a key driver, the study also found that creative execution factors like: Visual salience and clutter Branding clarity and consistency Storytelling and emotional resonance ...had a significant impact on garnering and sustaining user attention. Optimizing these creative dimensions represented an opportunity to amplify brand impact even further. The findings from this large-scale meta-analysis provide robust evidence that attention metrics like active viewable time are far more predictive of actual brand outcomes compared to standard digital ad delivery metrics. As the researchers summarize, "The results show a clear and direct correlation between attention and brand effects...a viewable impression alone is not enough to drive brand lift. Attention is a key ingredient. "For brands, this study highlights the importance of prioritizing ad placements, formats, and creative strategies that foster active consumer attention and engagement. Simply maximizing ad impressions and viewability is unlikely to move the needle on critical brand KPIs. As digital advertising continues to evolve with new ad tech and formats, metrics that quantify actual human attention will likely become even more crucial for maximizing advertising effectiveness and ROI. Harbine Engineering Journal Study on Ad Impact The study published in the Harbine Engineering Journal aimed to investigate the impact of advertisements on key metrics like brand awareness, customer satisfaction, and purchase intent. The researchers employed a multi-pronged approach that combined survey data with website analytics. 1. Survey Data Collection The core of the study involved conducting surveys across three distinct groups: Customers: A random sample of 5,000 customers who had made a purchase from the company's e-commerce website in the past 6 months. Employees: 250 employees across various roles and departments within the company. Management: 35 members of the senior leadership team and executive management. The surveys captured respondents' perceptions of the company's advertising campaigns, brand messaging, and overall customer experience. Specific questions focused on: Recalling and recognizing the company's ads across channels (TV, digital, print, etc.) Perceived impact of ads on brand awareness and purchase decisions Satisfaction with the website experience and e-commerce journey Likelihood to recommend the brand (Net Promoter Score) 2. Website Analytics Integration To complement the survey insights, the researchers also analyzed website behavior data captured via analytics tools like Google Analytics and Hotjar. Key metrics examined included: Traffic sources (organic, paid, referral, etc.) User journeys and conversion funnel performance Engagement signals (pages visited, time on site, clicks, etc.) Audience demographics (age, location, interests, etc.) The website data allowed the researchers to quantify actual user behavior and correlate it with the stated perceptions from the surveys. 3. Statistical Analysis The final stage involved rigorous statistical analysis to uncover potential relationships and drivers of advertising effectiveness. This included: Correlation Analysis: Examining the correlation between survey responses (e.g. ad recall) and website behavior metrics to identify potential links. Regression Modeling: Developing regression models to quantify the impact of factors like ad exposure, demographics, and website experience on key outcomes like satisfaction and purchase intent. Multivariate Testing: Analyzing differences in survey responses and website behavior across variations in the advertising creative/messaging using techniques like A/B testing. A key focus area was understanding if demographic factors like age had any bearing on the observed advertising impact and overall user experience. Key Findings of the Harbine Engineering Journal Study on Ad Impact The study yielded several noteworthy findings regarding the effectiveness of the company's advertising efforts: 1. No Correlation Between Age and Website Satisfaction Contrary to common assumptions, the analysis revealed no statistically significant correlation between a user's age and their stated satisfaction with the website experience. Both younger and older demographics exhibited similar levels of satisfaction, suggesting the website design and user experience were well-tailored to a broad audience. 2. Opportunities to Improve Ad Visibility and Targeting While overall ad recall was relatively high (68% for customers), the surveys highlighted opportunities to increase visibility and resonance of the advertising creative. Employees and management tended to overestimate the impact and memorability of the company's ads compared to actual customer perceptions. This misalignment signaled a need for more rigorous market testing and persona-based targeting of the advertising campaigns. 3. Positive Correlation Between Ad Exposure and Purchase Intent One of the most encouraging findings was a statistically significant positive correlation between survey respondents' recollection of seeing the company's ads and their stated likelihood to make a purchase. Customers who reported higher ad exposure and recall also tended to exhibit higher purchase intent scores and were more likely to recommend the brand. 4. Importance of Seamless User Experience The study found that a seamless, intuitive user experience on the company's website and e-commerce platform was crucial for maximizing the impact of advertising efforts. Respondents who reported frustrations or pain points during the purchase journey were far less likely to convert, regardless of their initial ad exposure and awareness levels. This underscored the need for consistent quality across all touchpoints in the marketing and sales funnel. While the study confirmed the overall positive impact of the company's advertising campaigns, it also highlighted several areas for optimization and improvement: Enhancing ad creative and messaging to boost visibility and memorability Leveraging more advanced audience segmentation and targeting capabilities Maintaining a relentless focus on delivering seamless user experiences Closing perception gaps between internal stakeholders and customers By combining robust survey data with actual user behavior insights, this study provided a comprehensive view into the real-world effectiveness of advertising initiatives. The findings can inform more impactful, ROI-driven strategies for allocating ad budgets and orchestrating cohesive cross-channel experiences. Columbia Business School: Factors influencing when advertising impacts brand awareness versus purchase intent The Columbia Business School meta-analysis aimed to synthesize research findings on the key factors that influence when advertising impacts brand awareness versus purchase intent. The researchers conducted a comprehensive review of over 200 papers published in major marketing journals between 1990-2012. Studies were included in the meta-analysis if they met the following criteria: Empirically measured the impact of advertising on either brand awareness metrics (e.g. recall, recognition) or purchase-related metrics (e.g. intent, choice, sales) Reported statistical estimates of advertising's effect size Provided details on key moderating variables like product category, ad content factors, media factors, etc. This rigorous screening process yielded a final sample of 57 studies spanning various product categories, countries, time periods, and methodologies (experiments, surveys, econometric models, etc.). The authors then coded each study along multiple dimensions: Dependent variable type (awareness vs. purchase) Product category (CPG, durables, services, etc.) Advertising content factors (rational vs emotional appeals, comparative ads, etc.) Advertising execution factors (repetition, media vehicle, spend/weight, etc.) Brand characteristics (quality, differentiation, market share, etc.) Methodological characteristics (data source, estimation technique, etc.) Meta-Analytic Approach The analysis involved calculating separate meta-analytic means of the advertising effect sizes for: Brand awareness metrics Purchase-related metrics This allowed comparing the overall magnitude of advertising's impact on each outcome type. The researchers also used meta-regression to examine how the different coded variables moderated advertising's effect on awareness versus purchase metrics. Weighted least squares regressions were specified with: Effect size as the dependent variable Coded study characteristics as predictors Weights based on the inverse of each effect's variance This approach quantified the influence of factors like product category, ad appeals, media factors, brand equity, etc. on advertising effectiveness. Key Findings of the Columbia Business School Meta Analysis on Factors Influencing when Advertising Impacts Brand Awareness versus Purchase Intent The meta-analysis yielded several important insights into the differential effects of advertising on building awareness versus driving purchase behavior: 1. Larger Effects on Awareness than Purchase Intent Across the studies analyzed, advertising had a significantly larger overall effect on enhancing brand awareness metrics compared to impacting purchase-related outcomes like choice and sales. The mean effect size for awareness was 0.32, versus just 0.16 for purchase metrics - about half as large. 2. Moderating Role of Advertising Weight As expected, higher advertising weight (measured by spend levels or repetition) increased advertising's ability to build awareness. However, it had a smaller positive impact on purchase metrics. This suggests that heavy ad spending is more effective for achieving widespread brand salience versus directly driving purchase behavior. 3. Importance of Brand Equity The analysis found that a brand's existing equity level was a key moderator of advertising effectiveness: For high-equity brands, advertising had a stronger positive effect on purchase metrics For low-equity brands, advertising provided a bigger boost to building awareness This highlights how brands may need to first invest in awareness-building before ads can effectively influence purchase. 4. Content Effects on Awareness vs. Purchase The use of emotional ad appeals and comparative advertising claims enhanced advertising's impact on purchase metrics more than awareness. Conversely, repetition of similar executions was more effective for reinforcing brand awareness versus driving purchase intentions. 5. Media Vehicle Differences The meta-regression revealed that different media vehicles had varying effects: Television had the largest positive impact on awareness Print media like magazines were most effective for influencing purchase Digital media had smaller effects than expected on both outcomes However, the authors note this may partly reflect the time period studied (pre-2012) when digital ad spend and measurement were much more limited. Overall, this comprehensive meta-analysis provides a nuanced perspective on how various factors moderate whether advertising achieves brand-building objectives versus more direct sales impacts. The findings can help guide strategic decisions around creative content, media planning, and budgeting based on specific brand/marketing objectives. They also highlight the importance of accounting for a brand's existing equity when setting advertising expectations and measuring effectiveness. Key Insights from the Studies Summarized Harvard Study on Search and Display Ad Interaction: Display ads significantly increase paid search conversions, but with a 2-week delay Both search and display ads exhibit strong positive carryover/dynamic effects over time Display ads also increase search ad clicks and costs, offsetting some of the conversion lift Accounting for attribution to search, dynamics, and added costs alters performance metrics The optimal budget allocation should increase search's share by up to 36% despite display's attribution benefit Nielsen Study on New Media's Brand Impact: Median brand awareness lift of 70% from podcast/influencer/branded content exposure Meaningful lifts also seen for favorability (28%), purchase intent (18%), short-term ROI (2.7x) High variance - top quartile saw 150%+ awareness lift, bottom quartile was negligible Attention and viewability were key drivers of effectiveness Lumen/Teads/Dynata Study on Attention and Brand Effects: Clear correlation between attentive ad exposure and upper-funnel brand metrics Lower-funnel metrics like purchase intent saw steep lift between 5-10 attentive seconds Attentive impressions drove 2-4x higher brand lift than just viewable impressions Creative optimization can further amplify attention and brand impact Harbine Engineering Journal Study on Ad Impact: No correlation between age and website satisfaction, suggesting good user experience Opportunities to improve ad visibility, targeting, and alignment with customer perceptions Positive correlation between ad exposure and purchase intent Seamless user experience crucial for maximizing ad effectiveness Columbia Business School Meta-Analysis: Advertising had larger effect on building awareness vs. driving purchase metrics Higher ad weight boosted awareness more for high-equity brands Emotional appeals and comparative ads enhanced purchase impact TV was best for awareness, print was best for purchase influence The studies highlight the importance of accounting for cross-channel attribution effects and carryover dynamics when evaluating online advertising performance. Display ads were found to significantly increase search ad conversions, but with a delayed impact, while both channels exhibited strong positive dynamics that improved effectiveness over time. However, display ads also drove up search costs. Accounting for these attribution and dynamic effects substantially altered performance metrics like revenue per ad dollar spent. The optimal budget allocation favored increasing the search budget share despite display's attribution benefit, due to search's stronger dynamics. Brands should prioritize ad placements and creative that foster active consumer attention and engagement to maximize impact on critical upper-funnel metrics like awareness and favorability. Leveraging advanced measurement techniques, predictive analytics, and an integrated cross-channel strategy can help marketers navigate attribution challenges, optimize media budgets, and orchestrate cohesive experiences that drive awareness and conversions. Sources Kireyev, P., Pauwels, K., & Gupta, S. (2013). Do display ads influence search? Attribution and dynamics in online advertising. Harvard Business School Working Paper, No. 13-070. Nielsen (2022). Measuring new media's impact on brand awareness and ROI. WARC (2024). New study shows a clear link between attention and brand effects. Harbine Engineering Journal (2024). An investigation into the impact of ads on brand awareness. Columbia Business School (2013). Factors influencing when advertising impacts brand awareness versus purchase intent: A meta-analysis. Faster Capital (2024). Exploring the latest research on enhancing brand awareness strategies.

  • Pipedrive Lead Scoring: Mastering Advanced Techniques for Sales Prioritization

    Setting Up Lead Scoring in Pipedrive Setting up an effective lead scoring system in Pipedrive CRM is crucial for sales productivity and prioritizing leads. The first step is to define lead scoring criteria that align with your business goals and ideal customer profile. There are several established models you can use as a starting point: BANT (Budget, Authority, Need, Timeline) CHAMP (Challenges, Authority, Money, Prioritization) MEDDIC (Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion) Alternatively, you can create custom criteria tailored to your specific sales process and buyer personas. Common factors to consider include: Company information (industry, size, location) Contact details (job title, department, seniority) Engagement signals (website visits, email opens, content downloads) Sales activities (meetings booked, proposals sent, follow-ups) Pain Points (product and service match) Once you've established the criteria, you'll need to create custom fields in Pipedrive to capture the relevant lead data. This could include details like: Pipedrive's Smart Contact Data feature can also help enrich lead profiles by automatically filling in details like: Company name, size, industry, location Contact name, job title, department Social media profiles Email address and phone number This data enrichment provides a more comprehensive view of each lead for accurate scoring and prioritization.With the scoring criteria defined and custom fields set up, you can start assigning scores based on the data in each lead's profile. For example: High Scores for: C-Level contacts at companies with 200+ employees Engaged leads who attended a webinar or requested a demo Prospects in your target industries with identified pain points Low Scores for: Contacts with generic roles like "Assistant" or no job title Companies outside your target verticals or size range Leads with no recent engagement or sales activities The key is to weight the criteria based on their importance and align the scoring thresholds with your definitions of a marketing qualified lead (MQL) and a sales qualified lead (SQL).By setting up lead scoring properly in Pipedrive, you'll be able to prioritize the hottest leads, automate lead routing, and ensure your sales team focuses on the most promising opportunities. This lays a solid foundation for sales acceleration, revenue growth, and maximizing Pipedrive's value as a lead management and sales automation tool. Advanced Lead Scoring Techniques in Pipedrive While the basic lead scoring setup in Pipedrive provides a good starting point, there are several advanced techniques you can implement to take your lead qualification and prioritization efforts to the next level: A. Assigning Scores Based on Web Visits and Email Engagement Pipedrive doesn't natively track website visits or email interactions, but you can integrate with third-party tools to capture this valuable engagement data and use it for lead scoring: Web Tracking Tools: Solutions like Leadfeeder, LeadLander, and Lead Forensics identify companies visiting your website based on their IP addresses. Email Tracking: Tools like Mailtrack, Yesware, and Mixmax provide open and click-through rates for your email campaigns. Higher scores can be assigned to leads who have visited key pages like pricing or engaged with your marketing emails, indicating stronger buying intent. B. Scoring Based on Pipedrive Activities and Custom Field Data In addition to the basic lead and company information, you can factor in Pipedrive activity data and custom field values when calculating lead scores: Activities: Assign higher scores for activities like meetings booked, proposals sent, contracts viewed, etc. Custom Fields: Score leads higher if they match your ideal customer profile based on custom fields like industry, job role, budget, etc. This allows you to prioritize leads who are actively engaged with your sales process and closely align with your target buyer personas. C. Implementing Lead Score Decay Over Time A common challenge is that lead scores can become stale if a previously hot lead goes cold. To account for this, you can implement lead score decay, which reduces a lead's score over time if there is no continued engagement.For example, you could set up an automation that decreases the lead score by 10% every 30 days of inactivity. This ensures your sales team focuses on the freshest, most engaged leads. D. Automating Actions Based on Lead Scores One of the biggest benefits of lead scoring is the ability to automate follow-up actions and lead routing based on the calculated scores: This ensures each lead gets the appropriate treatment based on their level of sales-readiness, freeing up your sales reps to focus on the hottest prospects.Additionally, you can set up internal notifications and task assignments to alert the right team members when a lead reaches a certain score threshold.By leveraging these advanced lead scoring techniques in Pipedrive, you'll be able to: Capture a more holistic view of lead engagement and buying signals Prioritize the hottest, most qualified leads for immediate follow-up Automate lead nurturing and routing for improved efficiencies Align follow-up actions with a lead's level of sales-readiness Ensure no hot leads slip through the cracks This level of sales automation and intelligent lead management can significantly improve your team's productivity, conversions, and overall sales performance. Integrating Pipedrive with Third-Party Tools While Pipedrive provides robust CRM and sales pipeline management capabilities, integrating with complementary third-party tools can supercharge your lead scoring efforts. Here are some powerful integrations to consider: A. Web Visitors Add-On to Identify Engaged Organizations The Web Visitors add-on for Pipedrive integrates with services like LeadFeeder and LeadLander to track which companies are visiting your website. This lead intelligence is invaluable for: Identifying engaged prospects before they even fill out a form Scoring leads higher based on website engagement signals Triggering automated follow-up actions for hot leads For example, you could set up an automation to assign a lead score of 75 and create a task for an SDR to reach out if a lead from your ideal customer profile visits your pricing page. B. Adinton for Identifying "Ready to Buy" Leads Adinton is an AI-powered lead scoring solution that integrates directly with Pipedrive. It uses machine learning to analyze data points like: Firmographic details (industry, company size, location) Contact details (job title, department, seniority) Buyer intent signals (website activity, content consumption) Sales engagement data (emails, meetings, proposals) Based on this analysis, Adinton calculates an Artificial Intelligence Score indicating how "ready to buy" each lead is. This score can then be synced to a custom field in Pipedrive for lead prioritization and automated routing. C. Salespanel and Outfunnel for Advanced Scoring Models Salespanel and Outfunnel are third-party lead scoring platforms that integrate with Pipedrive to provide more advanced scoring capabilities: Customizable scoring models based on your unique criteria Predictive scoring using machine learning algorithms Funnel analytics to identify drop-off points and optimize scoring Prescriptive recommendations for next best actions These tools are ideal if you want a more robust, data-driven approach to lead scoring beyond Pipedrive's out-of-the-box functionality. D. GetQuanty for Real-Time AI Lead Scoring GetQuanty is an AI-powered solution that integrates with Pipedrive to provide real-time lead scoring and prioritization. Its key features include: Automated data capture from web, email, CRM, and other sources Predictive lead scoring using machine learning models Intelligent lead routing and task automation Funnel analytics and optimization recommendations GetQuanty's AI engine continuously analyzes lead data and engagement signals to calculate real-time scores, ensuring your team always focuses on the hottest prospects.By integrating Pipedrive with best-in-class lead scoring and sales intelligence tools, you can take your lead management efforts to new heights: These integrations provide additional data points, predictive analytics capabilities, and intelligent automation to complement Pipedrive's core functionality. The result is a more comprehensive and effective lead qualification process to drive sales productivity, conversion rates, and ultimately revenue growth. Lead Scoring Best Practices Implementing an effective lead scoring system in Pipedrive requires careful planning and ongoing optimization. Here are some essential best practices to follow: A. Collaborating Between Sales and Marketing Teams Aligning your sales and marketing teams is critical for successful lead scoring. Marketing should define the criteria for a marketing qualified lead (MQL), while sales determines the sales qualified lead (SQL) threshold.Regular collaboration ensures the scoring model accurately reflects your ideal customer profile, buyer personas, and shared definitions of sales-readiness. B. Continuously Refining the Scoring Model Lead scoring is an iterative process. You'll need to continuously analyze performance data and refine your scoring criteria based on: Which leads are converting at higher rates Characteristics of your best customers Changing business priorities and target markets Set up a cadence (e.g. quarterly) to review and adjust the scoring model as needed. C. Focusing on Lead Behavior Over Demographics While demographic and firmographic data is important, research shows that behavioral data like email engagement and website activity is a stronger indicator of buying intent.Focus your scoring model primarily on lead engagement signals rather than just static company/contact details. D. Implementing Automation for Consistency Manual lead scoring is time-consuming and prone to human error. Leverage Pipedrive's workflow automation capabilities to ensure scoring rules are applied consistently across all leads.You can also automate actions like lead routing, task assignments, and follow-up sequences based on lead scores for improved efficiencies. E. Combining Multiple Scoring Models A single, one-size-fits-all scoring model may not be sufficient for complex businesses with diverse product lines or sales cycles.Consider implementing multiple scoring models tailored to specific use cases, product families, industries, etc. Then use the highest score across models to prioritize each lead. F. Setting Conversion-Ready Thresholds Work with your sales team to establish clear score thresholds for qualifying leads as sales-ready. For example: This ensures reps only pursue the hottest, most qualified opportunities. G. Implementing Lead Nurturing Workflows For leads that don't meet your sales qualification threshold, implement automated lead nurturing campaigns in Pipedrive to continue engaging them with relevant content until they're sales-ready.Nurturing helps prevent premature lead drop-off and can re-engage stale leads over time. H. Analyzing Lead Scoring Performance Regularly analyze key metrics like: Conversion rates by lead score range Average sales cycle duration by lead score Number of SQL and closed-won opportunities by lead score This data will help you identify potential issues and optimize your scoring criteria. I. Educating Teams on the Scoring Model Ensure all customer-facing teams thoroughly understand your lead scoring methodology. This shared knowledge enables consistent, effective follow-up based on a lead's level of sales-readiness.By following these best practices, you'll be able to implement a highly effective, data-driven lead scoring system in Pipedrive. This will empower your teams to prioritize the hottest prospects, automate processes, and ultimately drive more sales productivity and revenue growth. Limitations of Pipedrive Lead Scoring While Pipedrive provides robust CRM and sales pipeline management capabilities, its native lead scoring functionality has some notable limitations to be aware of: A. Lack of Native Lead Scoring Data Out of the box, Pipedrive doesn't capture many of the critical data points needed for comprehensive lead scoring, such as: Website activity (page visits, content downloads, etc.) Email engagement (opens, clicks, etc.) Detailed firmographic data beyond basic company info Technographic data (tools and technologies used) Intent data (search activity, content consumption, etc.) Without this valuable buyer signal data, your scoring model may not accurately reflect a lead's true level of engagement and sales-readiness. B. Limited Analytics Capabilities Pipedrive's reporting capabilities are fairly basic when it comes to analyzing the performance of your lead scoring efforts. There is no way to easily view metrics like: Conversion rates by lead score range Average sales cycle duration by lead score Revenue influenced by lead score Lead score trends over time This lack of robust sales analytics makes it challenging to optimize your scoring criteria for maximum impact. C. Complex Customization While Pipedrive allows you to create custom fields and basic automation rules, building out a truly sophisticated, multi-dimensional lead scoring model can become quite complex and unwieldy within the platform's constraints.For example, implementing capabilities like: Lead score decay over time Multi-model scoring (separate models per product/service) AI-powered predictive scoring Funnel analytics to identify drop-off points ...would likely require significant custom development and integration with third-party tools. D. No Predictive Lead Scoring Pipedrive's lead scoring is based solely on explicit, defined rules and criteria that you configure. It does not leverage machine learning or predictive analytics to automatically identify patterns and signals that may indicate a lead's propensity to convert.As a result, you may miss out on key buying signals that could improve your scoring accuracy and prioritization efforts.To overcome these limitations, you'll likely need to integrate Pipedrive with complementary third-party solutions for functions like: By augmenting Pipedrive with these additional capabilities, you can build a more robust, data-driven lead scoring and prioritization engine tailored to your specific business needs.It's important to weigh the costs and complexity of these potential solutions against the expected benefits of improved lead management, sales productivity, and ultimately better conversion rates and revenue performance. Additionally, you'll want to ensure any integrated tools can seamlessly sync data bi-directionally with Pipedrive to maintain a centralized view of your leads and sales pipeline.While Pipedrive's out-of-the-box lead scoring may have some shortcomings, its flexibility to integrate with complementary solutions makes it a powerful CRM platform at the center of your sales tech stack.

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