In today’s digital age, businesses are constantly seeking innovative ways to improve their lead generation strategies. Traditional methods can be time-consuming and may not always yield the desired results. However, with advancements in artificial intelligence (AI), businesses now have the opportunity to enhance their lead generation efforts and drive better outcomes. In this article, we will explore eight key ways to leverage AI to improve lead generation and propel your business forward.

Personalized Content Recommendations

AI-powered algorithms have the ability to analyze vast amounts of data to understand user preferences and behaviors. By leveraging AI, businesses can deliver personalized content recommendations to potential leads, increasing engagement and conversion rates.

AI algorithms can analyze a lead’s browsing history, social media activity, and other relevant data points to suggest content that aligns with their interests and needs. This targeted approach ensures that leads receive content that resonates with them, enhancing the overall customer experience and increasing the likelihood of generating quality leads.

Chatbots for Instant Engagement

AI-powered chatbots have revolutionized customer engagement by providing instant and personalized interactions. When integrated into lead generation strategies, chatbots can engage with website visitors, answer queries, and gather relevant information. Chatbots can use natural language processing to understand and respond to user inquiries, providing a seamless and efficient user experience.

By automating initial interactions, businesses can capture leads’ contact information and qualify them based on their responses. This not only streamlines the lead generation process but also ensures that leads receive prompt assistance, enhancing their overall experience with your brand.

Natural Language Processing for Lead Qualification

AI-powered natural language processing (NLP) techniques can help businesses automate lead qualification processes. NLP algorithms can analyze and extract information from leads’ responses, such as email inquiries or form submissions, to determine their level of interest and qualification.

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By automating lead qualification, businesses can save time and resources while ensuring that only the most qualified leads are pursued further. NLP can help categorize leads based on their intent, sentiment, and specific criteria, enabling businesses to prioritize follow-up actions and improve the efficiency of their lead generation efforts.

Predictive Lead Scoring

Lead scoring is a critical aspect of AI lead generation, as it helps businesses prioritize and focus their efforts on the most promising leads. AI-powered predictive lead scoring takes this process to the next level by using machine learning algorithms to analyze historical data and identify patterns that indicate lead quality.

These algorithms can analyze a wide range of data points, such as demographic information, past interactions, and purchase behavior, to predict a lead’s likelihood of converting. By leveraging AI for lead scoring, businesses can allocate their resources more effectively and focus on leads with the highest potential, improving overall conversion rates.

Automated Email Campaigns

Email marketing continues to be a powerful tool for lead generation. However, manually managing email campaigns can be time-consuming and prone to human error. AI-powered solutions can automate various aspects of email marketing, such as email scheduling, personalization, and segmentation.

AI algorithms can analyze lead data to determine the most appropriate time to send emails, personalize email content based on individual preferences, and segment leads into targeted groups for more relevant messaging. By automating these processes, businesses can optimize their email campaigns, deliver personalized experiences to leads, and increase the chances of converting them into customers.

Voice Search Optimization

With the increasing popularity of voice assistants and smart speakers, optimizing lead generation strategies for voice search is becoming essential. AI can help businesses adapt their content and SEO strategies to align with voice search queries. AI-powered algorithms can analyze voice search patterns and understand the intent behind queries to provide relevant and accurate information.

By optimizing content for voice search, businesses can increase their visibility in voice search results and capture leads who prefer using voice assistants for information retrieval.

Intelligent Lead Scouting

AI can also be leveraged for intelligent lead scouting, which involves identifying and targeting potential leads that match a specific set of criteria. AI algorithms can analyze large amounts of data from various sources, including social media platforms, business directories, and public records, to identify leads that meet predefined characteristics.

This approach helps businesses identify new and untapped markets, discover leads that may have otherwise gone unnoticed, and expand their reach. By using AI for intelligent lead scouting, businesses can uncover new opportunities and increase their chances of finding high-quality leads.

Data Analytics and Insights

AI-driven data analytics tools provide businesses with powerful insights into lead generation strategies. These tools can analyze vast amounts of data in real-time, uncovering patterns, trends, and correlations that human analysts may overlook.

AI algorithms can identify the most effective channels for lead generation, analyze customer behavior, and provide actionable recommendations for improving lead conversion rates. By leveraging AI-powered analytics, businesses can make data-driven decisions, optimize their lead generation efforts, and continuously improve their strategies based on actionable
insights.

Leveraging AI can significantly enhance lead generation efforts and drive better results for businesses.

By using AI to deliver personalized content recommendations, implementing chatbots for instant engagement, utilizing NLP and voice search optimization, leveraging predictive lead scoring and scouting, automating email campaigns, and utilizing AI-driven data analytics, businesses can optimize their lead generation strategies, improve conversion rates, and ultimately drive business growth.

Embrace the power of AI and unlock its potential to transform your lead generation efforts into a more efficient and effective process.

The post 8 Ways To Leverage AI To Improve Lead Generation appeared first on DigitalMarketer.

In the world of digital marketing, data is king. With so much information available, it can be overwhelming to try and make sense of it all. One of the best ways to gain insight into digital marketing trends is through the use of graphs.

Graphs can help to visualize complex data sets and identify patterns that may not be immediately apparent when looking at raw data. In this article, we will explore the top nine graphs for revealing digital marketing trends.

Line Graphs For Digital Trends

Line graphs are one of the most commonly used graphs in digital marketing. They are particularly useful for showing how a particular metric has changed over time. For example, a line graph could be used to show how website traffic has changed over the course of a year.

By plotting data points over time, it is easy to see any trends or patterns that may have emerged. Line graphs can also be used to compare data sets over time, such as comparing the performance of two different marketing campaigns.

Chord Diagrams Connecting Different Marketing Channels

Chord diagrams are a type of visualization that show the connections between different variables. They are often used to show the relationship between different parts of a complex system or network.

In digital marketing, chord diagrams can be used to show how different channels (such as social media, email marketing, and search engine marketing) are related to each other. By visualizing the connections between different channels, businesses can optimize their marketing mix and ensure that each channel is working together to achieve their marketing goals.

Scatter Plots for Digital Correlations

Scatter plots are often used in digital marketing to show the relationship between two different metrics. For example, a scatter plot, designed by a graph creator, could be used to show how the bounce rate on a website correlates with the time spent on the site. 

By plotting data points on an x and y axis, it is easy to see any correlations that may exist between the two metrics. Scatter plots can also be used to identify any outliers within a data set.

Bubble Charts Show How Differing Variables Relate to Each other

Bubble charts are similar to scatter plots, but they add a third variable to the mix by varying the size of the bubbles based on a third data point. This can be a useful way to visualize trends and patterns in complex data sets.

In digital marketing, bubble charts can be used to show how different variables (such as ad spend, click-through rate, and conversion rate) are related to each other.

Bar Graphs for Quick Comparisons

Bar graphs are another common graph used in digital marketing. They are particularly useful for comparing different data sets. For example, a bar graph could be used to compare the conversion rates of two different landing pages.

By presenting data in a visual format, it is easy to see which landing page is performing better. Bar graphs can also be used to compare data sets over time, such as comparing the number of leads generated by two different marketing campaigns.

Heat Maps Revealing Behavior

Heat maps are a unique type of graph that are particularly useful for analyzing website user behavior. Heat maps show how users interact with different parts of a website by using different colors to represent user engagement.

For example, a heat map could be used to show which parts of a landing page receive the most clicks. By analyzing heat maps, marketers can identify areas of a website that may need to be optimized to improve user engagement.

Pie Charts For Categorical Divisions

Pie charts are often used in digital marketing to show how a particular metric is divided among different categories. For example, a pie chart could be used to show how a company’s social media followers are divided among different age groups.

Pie charts are particularly useful for highlighting the most significant categories within a data set. However, it is important to keep in mind that pie charts can be difficult to read when there are too many categories.

Funnel Charts Reveal Bottlenecks

Funnel charts are a type of chart that shows how many users or customers move through a series of steps in a process. They are often used in digital marketing to track the conversion rate at each stage of a sales funnel.

By visualizing the drop-off rate at each stage of the funnel, businesses can identify potential roadblocks or bottlenecks in the conversion process and take steps to optimize their marketing strategy.

Gantt Charts for Keeping Campaigns on Schedule

Gantt charts are a type of bar chart that show the duration of each task in a project, as well as the start and end dates. They are commonly used in project management to track progress and deadlines.

In digital marketing, Gantt charts can be used to plan and track the progress of marketing campaigns. By breaking down a campaign into smaller tasks and assigning deadlines to each one, businesses can ensure that their marketing efforts stay on track and meet their goals.

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Conclusion

In conclusion, digital marketing is a complex field that requires businesses to track and analyze a large amount of data. Charts and graphs are essential tools for visualizing this data and identifying trends and patterns.

By using the right types of charts and graphs, businesses can gain insights into their marketing performance and make data-driven decisions to optimize their marketing strategy.

From line graphs and scatter plots to heatmaps and chord diagrams, there are a variety of charts and graphs that businesses can use to reveal digital marketing trends and stay ahead of the competition.

The post Data-driven Marketing: How Graphs & Charts Transform Digital Strategies appeared first on DigitalMarketer.

There are two core metrics that should drive a lot of the decisions you have in your organization; churn & sales. A great agency is constantly studying these two numbers diagnosing them from every angle learning specific areas of opportunity. 

The more you are able to understand these numbers and what they are composed of the better you’ll be equipped to making the right decisions for your business.

In this report, we want to look at churn, which is something we’ve been studying for about 10 years across two different agencies. The first one was scaled to over 1,000 clients and the second one we’ve scaled to over 200 full time employees in just 5 years. 

When you’re a young agency, churn is so important because 1-2 clients can represent a large portion of your income, however as you scale, the same is true. Imagine you’re an agency like Hite and you’re doing $500,000 per month in MRR.

If you have 10% churn monthly, you’ll need to do $50k in new sales just to break even. If you can create an environment where you’re more likely to have 5% churn, if you do $50,000 in sales you’ll grow by 5%.

Understanding why clients leave and acting on it, isn’t only the key to scaling. Agencies with lower churn, partake in other benefits such as receiving more referrals & a much higher evaluation when it comes to selling the business. 

Hite is constantly focused on understanding the why behind our growth & this is essential for your business if you want to scale in 2023.

Churn is critical, especially as you scale for churn is a representation of the quality of your product, service, & customers.

Every agency is constantly battling both the increase of sales and the decrease of churn.

Defining Churn? 

Churn can be broken down in a lot a ways, but for agencies, the most common two churn metrics you’ll see is Client Churn & Financial Churn. These two churn types can be define these two churns as followed: 

For Client Churn we will look at the monthly turnover of clients regardless of financial impact.

For example, If in January you had 10 clients pay you then in February only 8 of them paid you, that would be a turnover of 2 clients and equal 20% churn. In this example it would not matter how much each client represented financially. 

For Financial Churn, we look at the monthly turnover of revenue regardless of clients.

For example, if in January you had $20,000 in recurring collected MRR and in February you only collected 18,000 of that $20,000, it would represent a 10% churn rate. 

Understanding the difference between these two numbers is crucial, let’s look at the following list of clients. 

MRR

Client A $1,000

Client B $5,000

Client C $2,000

Client D $3,000

If we were to lose Client B, you would have 25% client churn, however you’d have 50% financial churn. There could be a very large difference in these numbers especially as you scale. 

The Problem With Researching Churn

Doing research on churn for agencies doesn’t come easily. First off, about 80% of agencies that exist today would be defined as micro agencies, doing less than $15,000 in monthly revenue of which the vast majority do not keep up with, nor have any data on their numbers, especially when it comes to churn. 

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If you take into consideration those that do keep great track of their numbers, between those they may manage and report back churn in many different ways, even beyond the above numbers.

For example, there is a well known agency that is doing several $100m in annual revenue that keeps track of their financial churn, but in their own way focusing more on net growth vs. churn.

In their model, they look at how much was lost, and measure that against what was upsold in order to come up with a net churn. 

With that said, we believe that this report takes all those data points into consideration arriving to tangible and definitive results.

The post How to Reduce Churn appeared first on DigitalMarketer.

2022 was not an easy year, with a lot of declines in key metrics, particularly in the middle of the year. Q4 gave us a reason for optimism though, so will the momentum keep going or will 2023 continue financial uncertainty? 

Last year was a watershed moment in the history of ecommerce. While the 2010s saw the rapid expansion of online shopping thanks to developments in mobile devices, the expansion of social media influence, and a massive shift in consumer behavior, this decade will see an even greater change thanks to AI.

Here’s what we found.

How We Aggregate:

Hawke Media uses the data from its proprietary marketing technology platform, HawkeAI. HawkeAI aggregates data across 1000s of businesses’ marketing channels and $100’s of millions in annual media spend to compile these data benchmarks. 

Ecommerce Businesses:

Web Analytics 

• Revenue was down 5% YoY, which predominantly came from declines in Q2 and Q3 (Q3 was down 16%). We saw stable numbers for Q4 YoY.
  • Question for 2023: Does Q4 stabilization represent a broader trend of moving towards growth and predictability, or was it simply a deal-driven BFCM period (which was up 16% compared to BFCM 2021) that is propping up an otherwise stagnant revenue report? 
• Average order value (AOV) increased significantly YoY (31%). A portion of this is likely due to inflation, as the AOV increased as 2022 went on, as opposed to 2021 where AOV was consistent throughout the year, even during the Q4 peak retail period.
• Another potential reason for an increase in AOV is a continued emphasis on buy-now-pay-later usage. We saw a 78% increase in buy-now-pay-later usage during BFCM despite rising interest rates.
  • Question for 2023: Will the rising cost of debt curtail the use of BPNL option, and ultimately curtail inflation in general? Using bundling and complementary product recommendations (‘you may also like…’) will be key in maintaining/growing AOV without simply increasing prices. 
• Sessions overall were down 5%, which aligns with the revenue decline as well, while the bounce rate held steady.
  • Question for 2023: As Google Analytics switches to engagement-based metrics it will be interesting to see what metrics and benchmarks will be found in order to assess the quality of a website’s traffic. The simplest answer is of course transactions! 

• The quality of those sessions was also down, as transaction rate decreased by 24%. This was offset by the AOV increase (i.e. those that did buy, spent more). These transaction rates were bottoming out at 2% in Q2 and Q3 of this year, compared with all quarters of 2021 being above 3%.
  • Question for 2023: With media budgets tightening, optimizing web traffic to conversion is crucial. Where/how brands invest to generate a more optimized site will be key (checkout process, site speed, landing page/promos, etc.). 

Organic Channels

• Email marketing saw a gradual decline over the course of 2022 on multiple performance metrics, including both quantity of sessions and quality of sessions. Total sessions from email declined 12% but were flat for most of the year until Q4. Similarly, transaction rates on email declined from 4% to 3% YoY.
  • Question for 2023: With these declining results, how can brands attract new email sign-ups and tailor content to not see high unsubscribe rates? 

• Organic social content also took a hit this year, with both sessions and transaction rates down. Sessions particularly have been on a steady decline since the start of 2021, decreasing almost every quarter (except for Q4 naturally). This could be indicative of either less content being produced, or audiences that are more particular or selective in what they click on as pandemic restrictions lift and people are not surfing social media the same. Of course, the other possibility is a continued challenge in attribution from various updates to tracking. 
• Affiliate was a bright spot for 2022, with a 16% increase in sessions and a 35% increase in transactions. 

Paid Channels 

• Google Ads
  • Spend YoY increased 3%, but distribution was very different
    • 2021 saw a linear increase quarter over quarter in spend 
    • 2022 saw a significant drop off in Q3, and while Q4 increased over Q3, Q4 YoY was down 15% 
    • Media budgets were definitely impact by economic climate and lifting of restrictions 
    • Question for 2023: with a year of hopefully no restrictions, and continued economic uncertainty, where will ‘the bottom’ be in terms of spend, when will we see the ramp up? 
  • Clicks moved in line with spend (up 4%), with predictability/steadiness in CPCs YoY, which is helpful for forecasting in uncertain times. Using a ‘bottom-up’ approach of starting with a CPC type metric to establish sessions expected from Google is likely more reliable than doing a ‘top-down’ approach to forecasting (i.e. where are we going to generate $X of revenue from) 
  • Eyeballs got more expensive on key visual networks for Google (YouTube and Display), with increases of around 30% in CPMs on those networks. Ultimately both also had lower conversion rates (approx. 15% drop in conversion rates on both).
    • These CPMs are still lower than the typical social media platforms, so they still represent a cost-effective option to generate impressions.
• Meta Ads
  • Meta Ad spending also increased 4% YoY, but was more linear in growth, with spend increasing each quarter over quarter in 2022. 
  • Meta Ads also sees a more significant drop off from Q4 to Q1 than Google (Facebook dropped off 14%, Google dropped 4%). This is likely indicative of more seasonality in spending on Meta Ads during peak retail, whereas Google is seen as more of the ‘baseline’ spend to capture highest intent. 
  • Question for 2023: Will we see this same drop off in spend in Facebook Q1 this year as last year, or will the positive CPMs/CPCs from Q4 on Facebook mean advertisers stay with the platform? 
  • The increase in spend on Facebook was almost entirely due to a YoY increase in Facebook spend in Q4, while the rest of the year was mostly flat. This is likely the result of the 64% and 47% YoY increases we saw in spending on TikTok and Pinterest respectively. 
  • Question for 2023: How much of the diversification of social media advertising will continue? The numbers would suggest that any new budgets are being allocated to these new platforms and that Google and Facebook budgets are being treated as optimized/maxed-out. 
• Other platforms
  • CPMs on TikTok and Pinterest are increasing as more budget shifts to these platforms. For example, CPMs on TikTok increased from $4 to $8 YoY. The CPAs still though are lower than Meta, so until those become more aligned it is likely that these platforms will take more of any increases in ad spend.
    • Question: When will these platforms reach the saturation point and competition of Meta and Google? Based on these trends, we would expect to see that by end of 2023.

“Non-discretionary” Ecommerce: 

Includes: food/drink, healthcare, B2B

Web Analytics 

• The conversion rate from visits to the site increased from 2.6% 2.9% YoY. This is indicative of two key points:
  • Once people land on a site with products of this nature (i.e. items that are more essential or inelastic in demand), they have a higher likelihood of purchasing than sites with more discretionary products, which had a conversion rate of 2.2% in 2022). 
  • There were also fewer sessions for website selling these products, so while conversion rates were up, total transactions declined by 6%. 
• Another telling piece of information is the average order value declined YoY, so even though those sessions had higher intent to purchase, the average sale was worth less. This is potentially due to price sensitivity from economic conditions where these products are needed but easily substituted for a lower cost alternative. 

Organic & Paid Channels

• To support the idea of shoppers looking for lower cost alternatives, we saw an interesting trend in where sessions came from.
  • Typically more ‘loyalty’ based channels such as email, direct, (i.e. someone opens a browser and goes to that page), and organic search were all down YoY. 
  • Conversely, sessions increased from paid search, social and referral sources. These channels can typically be attributed to more people searching out for new alternatives, as opposed to just automatically buying from known brands. 
• Unsurprisingly, conversion rates associated with the more loyalty based channels stayed consistent, with all of them staying within .2% of their previous year number (i.e. someone who clicks on an email from a brand they know were just as likely to buy from that brand in 2021 as 2022, but fewer people were clicking in the first place).
• Also perhaps unsurprisingly, but validates the conclusions above, is that the ‘browsing’ channels of social and referral that drove more traffic, saw declines in conversion rates as more people were searching out new buying options.
  • The channel that bucked this trend was paid search, which saw sessions go up 8% and still maintained a conversion rate YoY (3.6%) 
  • That said, social and referral conversion rates, while down from 2021, were still above paid search (both around 5.5%). 
  • This could be an indicator that buyers looking for alternatives ‘trust’ or find better recommendations from those social or referral sources than a plain old google search (i.e. perhaps they trust social proof more than google’s search algorithm)

“Discretionary” Ecommerce: 

Includes: arts/entertainment, beauty, fitness, home and garden and apparel) 

Web Analytics 

• discretionary products, which had a conversion rate drop from 2.5% (which was in line with non-discretionary in 2021) down to 2.2% in 2022.
  • This makes sense given the economic climate that buyers are going to ‘shop around’ and also makes sense that discretionary items are purchased less frequently than non-discretionary
• BUT, what is striking within the data is that the average order value of purchasing discretionary items went up by 20%, so while conversion rates dropped, those that bought spent more money. As discussed above, there are a few possibilities for this, including buy-now-pay-later usage.
  • This is especially interesting since the spike in AOV is heavily skewed to Q4 of this year. AOV was up 41% in Q4 of 2022 compared to Q4 of 2021, which cannot be explained away by inflation. For Q1+Q2 2022, AOV was only up 9% compared to Q1+Q2 2021, which is very closely correlated with inflation rates (around 7% YoY). 
  • Question for 2023: Was this spike in Q4 pent up demand from people tightening spending the rest of the year, or is this going to continue? 

Organic Channels

• The only channel that saw an increase in sessions and transactions was affiliates. Given the discretionary nature of these products, it likely makes sense that affiliate marketing performs well since affiliate marketing predicates itself on social proof, testimonials, and are often more creative in nature from a copy/visual perspective. 
• Email had an interesting mix in that sessions were actually up, but revenue and transactions were down. So, unlike non-discretionary goods, consumers were still willing and active in engaging with emails, but were not able to be converted. The most telling stat to this point is that in Q4 2021 email had a conversion rate of 4.4%, and in Q4 2022 that fell all the way to 2.7%, meaning that shoppers were looking for the deals, and were more selective on what deals they actioned.
  • This is substantiated by an AOV increase in Q4 2022 compared to Q4 2021 of 25%, so when they did find a deal they liked, they took action in a big way!  
  • Question for 2023: How often can full retail price be realized? Are shoppers only willing to pull the trigger on deals? How can you structure your sales/products to maximize average order value?  

Paid Channels

• We saw declines in ad spending on both Facebook and Google, but Facebook’s was more significant (27% decline) vs Google (4% decline).
  • Facebook did see improvements in CPM, CPC and CPA as a result of this decline in spend (less competition). As mentioned above, this spend was reallocated to other social platforms. 
  • What is interesting is conversions reported actually went up 7%, showing that Facebook had likely reached a point of diminishing returns and inefficient. By peeling back the spend a bit, the more efficient/likely buyers still engaged and bought. 
  • Question for 2023: are there more efficiencies to be gained by shifting spend, ro are the other platforms soon going to reach a tipping point of saturation themselves? Let this be a case study/lesson in the inefficiency of not diversifying your spend enough!

Lead-Generation Businesses  

Quick note: the definition of a ‘goal completion’ or ‘conversion’ when it comes to lead-gen is greatly varying and subjective to each individual business.

Web Analytics 

• Goal completions YoY for the first three quarters of the year were up a modest 6% until Q4 which was significantly higher (28%). This is impressive given sessions and bounce rates were relatively flat YoY. In other words, those that went, had intent as indicated by an increase in goal completion rate.
  • Question for 2023: With the switch to Google Analytics 4, these benchmarks will become challenging to monitor and the definition of ‘goal completion’ will become morphed into event-based actions. No one really knows what will happen but having GA4 set-up and running on sites today is imperative to get some sense of baseline performance under the new system. 

Organic Channel 

• Visitors had more intent across multiple channels, which is the opposite of ecommerce trends. For lead-gen businesses, both email and social content generated more goal completions than 2021 (21% and 25% respectively). This is despite social having a 24% decline in sessions.
  • Question: Is the decline in sessions due to tracking limitations? Or is it simply consumer sentiment? 
• Ultimately, seeing the goal completions increasing is the key benchmark to look at. 

Paid Channel 

• Google Ads

• YoY was up 3% as well, but Q4 was the lowest spend since Q1 of 2021. This is despite very good results overall from the spending, with conversions from Google Ads improving YoY in every quarter of 2022. 
• Question for 2023: What will be needed to ever get brands to invest heavier in Google Ads Network, or is there any appetite at all? Is it saturated? Results would suggest investing in it…Another trick though is performance max campaigns. While they are reporting increases in conversions on performance max campaigns, are those leads becoming sales at the point of sale or in your CRM? Marketers have seen mixed results from these campaigns to date.
  • This concern is backed up by a reported 25% increase in conversion rate on cross-network campaigns, which has (artificially?) driven down CPAs by 36% YoY.. 

• Meta Ads

• Meta spend decreased 13% YoY, but this was dramatically split between the year. In the first half of 2022, the spend was very consistent with the spend in the first half of 2021. However, in the second half the spend declined 28% YoY. Those that stayed invested on the platform though did see declining CPMs (14% decrease) and CPCs (5%). 
• Pinterest saw an increase in spend of 10%, and TikTok increased as well, which likely drove the decrease from Meta. Pinterest still does have a lower CPM than Meta ($6 compared to $7), but that gap has closed from $5 and $8 respectively last year. The gap in cost per click has also closed between TikTok/Pinterest and meta.

Lead-Generation Businesses – B2B specific

Quick note: the definition of a ‘goal completion’ or ‘conversion’ when it comes to lead-gen is greatly varying and subjective to each individual business.

Web Analytics & Organic Channels

• Despite the increase in paid media that we discuss below, sessions YoY were within 2% of last year’s total. This indicates that the spend did not have a significant impact on overall traffic. 
• Goal completion rates as well were also down about 10%, which indicates that the increased spend and re-allocation of budget did not necessarily lead to higher quality traffic hitting the site. 
• A B2B favorite for marketing is email of course. We did see a 13% increase in sessions that came from email, but a goal completion rate that declined by 25%. If the goal of email marketing is to generate brand awareness and engagement with the content that you are sharing (e.g. monthly newsletters, product updates, etc) then mission accomplished for B2B marketers this year, from a loyalty and engagement standpoint email worked!
  • If you were trying to use email marketing to get prospects to fill out a lead gen form or activate a trial, then overall that is not what email performance was delivering. 
• orOrganic search was a similar story in some regards, we saw an improving bounce rate on organic search sessions, so people were finding the content they wanted more often and engaging with it, but goal completion rate for organic search was down. Again, that ‘goal completion’ may not be the objective and with long sales cycles, etc it is tough to conclude on this but from a direct attribution perspective of someone read a blog and then signed up to be pitched services was not happening as frequently as I’m sure some marketers would like. 

Paid Channel 

• Let’s actually talk about LinkedIn here, since this is effectively the only category of businesses that care about it  
• LinkedIn from an engagement with ads perspective did well in 2022. While media spend did increase by 25%, we saw a larger increase in clicks (i.e. CPC actually went down) and an improvement in click-through rate as well.
  • To compare this to the movement in ‘conversions’ is tricky knowing that B2B sales cycles can be longer and the definition of a conversion stops at the website experience and rarely be truly connected back to the real source of truth for B2B companies, the almighty CRM! 
  • For what it’s worth, we did actually see conversions stay flat despite this increase in clicks.
• Across Google and Facebook we did also see increases in media spend. The google increase was spread across all google ad networks and not concentrated to any one tactic, indicating this was the bi-product of broadly applied budget changes and not a tactic or result specific re-allocation.
  • Question for 2023: Will increased spending in digital continue in our new virtual norm, or as restrictions are lifted will spend get redirected back to the traditional B2B staples of trade shows and in-person sponsorships/networking? 
• While spending did increase overall in 2022, it was not linear by any means. The increase was predominantly generated by increases in Q1 and Q2 2022 compared to 2021. For example, Google spending for H1 increased by 40% (remember Jan-June 2021 was still very much pandemic-restricted and plenty of uncertainty around supply chains, etc still existed). Conversely, H2 spending was flat YoY.

Overall, from 2022 there are a few takeaways:

• Customer Lifetime Value is more important than ever – with the cost of paid media increasing, it’s more important than ever to keep the customers you get. This means improving product/service quality, enhancing customer experience, and maintaining contact with customers between purchases by providing value-adding information through content.

• You MUST HAVE a cohesive and comprehensive marketing strategy – With AI tools increasing the productivity of marketing managers and allowing all marketers to produce content faster than ever while also managing paid media channels more effectively, the marketer that can produce the best overall strategy will win.

• You need to dial in ALL CHANNELS – As you can see with the data, the effectiveness of paid media, email marketing, and social media is shifting widely between platforms. This means that predicting which channel to focus on will be more difficult. The best marketers will need to both understand and execute cohesive campaigns that span multiple channels to ensure that their message gets through.

• Position your products as a “must-have” – Wallets are a little tighter, and decisions are being made on what is a “must-have” purchase, and what is a “nice-to-have”. In difficult economic times like we’re experiencing currently, customers are looking for what they can cut. The better you can position your products and services as necessary to the customer, the better off your bottom line will be. When in doubt, show how your products can do one or more of the big three for your customers: Save them time, Save (or make) them money, Improve their quality of life

• Get strategic about customer acquisition – With ROAS becoming less predictable and having potentially longer timelines before proving profitable, it’s a good idea to offset your customer acquisition costs by forming strategic partnerships, affiliates, and influencers who can provide new customers regularly at breakeven or better to remain cash flow positive.

The post Ecommerce Data Benchmark Report 2023 appeared first on DigitalMarketer.

Understanding and tracking the performance of your marketing efforts is essential in order to grow your business. While there are dozens of metrics you can use to measure the success of your campaigns, some are more important than others. In this article, we’ll provide an overview of the ten most important marketing metrics that everyone should be aware of, and most likely tracking on a weekly basis.

(Before we dive in, if you want a shortcut to building your growth marketing scorecard that tracks all of your most important metrics in one place, you can steal our growth marketing scorecard here >>)

With the understanding that every business is different, and a couple of these metrics might not make sense for you to track, here are the top 10 metrics most Heads of Marketing should track:

#1: Unique Pageviews

Unique pageviews measure how many times a webpage has been visited by individual users over the course of a designated period. This metric helps you understand which content resonates with your audience and how often they view it. It also provides insight into user engagement and helps inform decisions about what kind of content to prioritize in future campaigns.

#2: % New Visitors

% new visitors measures how many unique visitors have come to your website for the first time during a given period. This metric is helpful in understanding whether or not you’re successfully reaching new audiences with each campaign and can help inform decisions about where to allocate resources for maximum impact.

#3: Video Watch %

Video watch % measures how often people watch a video all the way through, as opposed to how many times it’s been viewed or clicked on. This metric gives you an indication of user interest and helps inform decisions about whether or not your audience is finding value in your videos or if they’re being ignored.

#4: Click Through Rate (CTR)

CTR measures the number of clicks on a particular link compared to the number of impressions (how many times that link was seen). A high CTR indicates that users find the link interesting enough to click on it while low CTR numbers suggest there might be improvements needed within the content itself or its placement within a webpage or email message.

#5: Open Rate

Open rate measures how often people who receive an email open it, as opposed to leaving it unread in their inboxes, also known as “opens” vs “bounces” rate. Understanding this metric helps marketers decide whether their messages are engaging enough for readers to take action on them and can help inform decisions about subject line wording, email length, and other aspects related to emails sent out through campaigns.

#6: Leads Generated

Leads generated measures how many individuals expressed interest in learning more about a product or service by taking an action such as filling out a form or signing up for an event. Tracking this metric is important for evaluating the effectiveness of various channels and campaigns used for lead-generation activities.

#7: Revenue

Tracking total revenue generated from campaigns provides insight into overall performance and ROI from those efforts. It also helps marketers understand which channel offers more significant returns so they can adjust their strategy accordingly.

#8: Number Of Lead Magnets Downloaded

Lead magnet downloads measure how many individuals downloaded a valuable piece of content offered by a company in exchange for contact information such as name, email address, etc. These metrics provide insight into user engagement with various lead magnets created by organizations so they can evaluate which ones resonate best with their target audiences and create more effective strategies going forward.

#9: North Star Metrics Performance

North Star Metrics are one high-level key performance indicator (KPI) designed to keep organizations focused on achieving their long-term goals regardless of short-term successes or failures — measuring performance against these metrics paints an accurate picture regarding progress towards achieving desired results over time.

#10: Upsell Take Rate

Upsell take rate measures conversion rates when customers are presented with opportunities to purchase upgraded versions/features after purchasing initial products/services — understanding this metric helps companies identify areas where they could improve customer experience and increase the chances of customers taking advantage of upsell opportunities available.

The post Top 10 Metrics Most Heads of Marketing Should Track appeared first on DigitalMarketer.

How SMART checks can help your metrics.

Amara Omoregie, Founder & CEO of amaraREPS talks about how to review your growth scorecards for maximum results.

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The post How SMART Checks Can Help Your Metrics – Amara Omoregie [VIDEO] appeared first on DigitalMarketer.