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• 7 Benefits Segmentation Examples + How to Get Started

  26 mars 2024, par Erin

  Every copywriter knows the importance of selling a product’s benefits, not its features. So why should your marketing efforts be different ?

  Answer : they shouldn’t.

  It’s time to stop using demographic or behavioural traits to group customers and start using benefits segmentation instead.

  Benefits segmentation groups your customers based on the value they get from your product or service. In this article, we’ll cover seven real-life examples of benefits segmentation, explain why it’s so powerful and show how to get started today.

  What is benefits segmentation ?

  Benefits segmentation is a way for marketers to group their target market based on the value they get from their products or services. It is a form of customer segment marketing. Other types of market segmentation include :

  • Geographic segmentation
  • Demographic segmentation
  • Psychographic segmentation
  • Behavioural segmentation
  • Firmographic segmentation

  Customers could be the same age, from the same industry and live in the same location but want drastically different things from the same product. Some may like the design of your products, others the function, and still more the price. 

  Whatever the benefits, you can make your marketing more effective by building advertising campaigns around them.

  Why use benefits segmentation ?

  Appealing to the perceived benefits of your product is a powerful marketing strategy. Here are the advantages of you benefit segmentation can expect :

  

  More effective marketing campaigns

  Identifying different benefits segments lets you create much more targeted marketing campaigns. Rather than appeal to a broad customer base, you can create specific ads and campaigns that speak to a small part of your target audience. 

  These campaigns tend to be much more powerful. Benefits-focused messaging better resonates with your audience, making potential customers more likely to convert.

  Better customer experience 

  Customers use your products for a reason. By showing you understand their needs through benefits segmentation, you deliver a much better customer experience — in terms of messaging and how you develop new products. 

  In today’s world, experience matters. 80% of customers say a company’s experience is as important as its products and services.

  Stronger customer loyalty

  When products or services are highly targeted at potential customers, they are more likely to return. More than one-third (36%) of customers would return to a brand if they had a positive experience, even if cheaper or more convenient alternatives exist.

  Using benefits segmentation will also help you attract the right kind of people in the first place — people who will become long-term customers because your benefits align with their needs. 

  Improved products and services

  Benefits segmentation makes it easier to tailor products or services to your audiences’ wants and needs. 

  Rather than creating a product meant to appeal to everyone but doesn’t fulfil a real need, your team can create different ranges of the same product that target different benefits segments. 

  Higher conversion rates

  Personalising your pitch to individual customers is powerful. It drives performance and creates better outcomes for your target customer. Companies that grow faster drive 40 per cent more revenue from personalisation than their slower-growing counterparts.

  When sales reps understand your product’s benefits, talking to customers about them and demonstrating how the product solves particular pain points is much easier. 

  In short, benefits segmentation can lead to higher conversion rates and a better return on investment. 

  7 examples of benefits segmentation

  Let’s take a look at seven examples of real-life benefits segmentation to improve your understanding :

  Nectar

  Mattress manufacturer Nectar does a great job segmenting their product range by customer benefits. That’s a good thing, given how many different things people want from their mattress. 

  It’s not just a case of targeting back sleepers vs. side sleepers ; they focus on more specific benefits like support and cooling. 

  

  Take a look at the screenshot above. Nectar mentions the benefits of each mattress in multiple places, making it easy for customers to find the perfect mattress. If you care about value, for example, you might choose “The Nectar.” If pressure relief and cooling are important to you, you might pick the “Nectar Premier.”

  24 Hour Fitness

  A gym is a gym is a gym, right ? Not when people use it to achieve different goals, it’s not. And that’s what 24 Hour Fitness exploits when they sell memberships to their audience. 

  As you can see from its sales page, 24 Hour Fitness targets the benefits that different customers get from their products :

  

  Customers who just care about getting access to weights and treadmills for as cheap as possible can buy the Silver Membership. 

  But getting fit isn’t the only reason people go to the gym. That’s why 24 Hour Fitness targets its Gold Membership to those who want the “camaraderie” of studio classes led by “expert instructors.”

  Finally, some people value being able to access any club, anywhere in the country. Consumers value flexibility greatly, so 24 Hour Fitness limits this perk to its top-tier membership. 

  Notion

  Notion is an all-in-one productivity and note-taking app that aims to be the only productivity tool people and teams need. Trying to be everything to all people rarely works, however, which is why Notion cleverly tweaks its offering to appeal to the desires of different customer segments :

  

  For price-conscious individuals, it provides a pared solution that doesn’t bloat the user experience with features or benefits these consumers don’t care about.

  The Plus tier is the standard offering for teams who need a way to collaborate online. Still, there are two additional tiers for businesses that target specific benefits only certain teams need. 

  For teams that benefit from a longer history or additional functionality like a bulk export, Notion offers the Business tier at almost double the price of the standard Plus tier. Finally, the Enterprise tier for businesses requires much more advanced security features. 

  Apple

  Apple is another example of a brand that designs and markets products to customers based on specific benefits.

  

  Why doesn’t Apple just make one really good laptop ? Because customers want different things from them. Some want the lightest or smallest laptop possible. Others need ones with higher processing power or larger screens.

  One product can’t possibly deliver all those benefits. So, by understanding the precise reasons people need a laptop, Apple can create and market products around the benefits that are most likely to be sold. 

  Tesla

  In the same way Apple understands that consumers need different things from their laptops, Tesla understands that consumers derive different benefits from their cars. 

  It’s why the company sells four cars (and now a truck) that cover various sizes, top speeds, price points and more. 

  

  Tesla even asks customers about the benefits they want from their car when helping them to choose a vehicle. By asking customers to pick how they will use their new vehicle, Tesla can ensure the car’s benefits match up to the consumers’ goals. 

  Dynamite Brands

  Dynamite Brands is a multi-brand, community-based business that targets remote entrepreneurs around the globe. But even this heavily niched-down business still needs to create benefit segments to serve its audience better. 

  It’s why the company has built several different brands instead of trying to serve every customer under a single banner :

  

  If you just want to meet other like-minded entrepreneurs, you can join the Dynamite Circle, for example. But DC Black might be a better choice if you care more about networking and growing your business.

  It’s the same with the two recruiting brands. Dynamite Jobs targets companies that just want access to a large talent pool. Remote First Recruiting targets businesses that benefit from a more hands-on approach to hiring where a partner does the bulk of the work.

  Garmin

  Do you want your watch to tell the time or do you want it to do more ? If you fall into the latter category, Garmin has designed dozens of watches that target various benefits.

  

  Do you want a watch that tracks your fitness without looking ugly ? Buy the Venu. 

  Want a watch designed for runners ? Buy the Forerunner. 

  Do you need a watch that can keep pace with your outdoor lifestyle ? Buy the Instinct. 

  Just like Apple, Garmin can’t possibly design a single watch that delivers all these benefits. Instead, each watch is carefully built for the target customer’s needs. Yes, it makes the target market smaller, but it makes the product more appealing to those who care about those benefits.

  How to get started with benefits segmentation

  According to Gartner, 63% of digital marketing leaders struggle with personalisation. Don’t be one of them. Here’s how you can improve your personalisation efforts using benefits segmentation. 

  Research and define benefits

  The first step to getting started with benefit segmentation is understanding all the benefits customers get from your products. 

  You probably already know some of the benefits, but don’t underestimate the importance of customer research. Hold focus groups, survey customers and read customer reviews to discover what customers love about your products. 

  Create benefit-focused customer personas

  Now you understand the benefits, it’s time to create customer personas that reflect them. Group consumers who like similar benefits and see if they have any other similarities. 

  Price-conscious consumers may be younger. Maybe people who care about performance have a certain type of job. The more you can do to flesh out what the average benefits-focused consumer looks like, the easier it will be to create campaigns. 

  Create campaigns focused on each benefit

  Now, we get to the fun part. Make the benefit-focused customer personas you created in the last step the focus of your marketing campaigns going forward. 

  Don’t try to appeal to everyone. Just make your campaigns appeal to these people.

  Go deeper with segmentation analytics

  The quality of your benefit segmentation strategy hinges on the quality of your data. That’s why using a an accurate web analytics solution like Matomo to track how each segment behaves online using segmentation analytics is important.

  

  This data can make your marketing campaigns more targeted and effective.

  Benefits segmentation in practice

  Let’s say you have an e-commerce website selling a wide range of household items, and you want to create a benefit segment for “Tech Enthusiasts” who are interested in the latest gadgets and cutting-edge technology. You want to track and analyse their behaviour separately to tailor marketing campaigns or website content specifically for this group.

  1. Identify characteristics : Determine key characteristics or behaviours that define the “Tech Enthusiasts” segment. 

  This might include frequent visits to product pages of the latest tech products, site searches that contain different tech product names, engaging with tech-specific content in emails or spending more time on technology-related blog posts.

  One quick and surefire way to identify characteristics of a segment is to look historically at specific tech product purchases in your Matomo and work your way backwards to find out what steps a “Tech Enthusiast” takes before making a purchase. For instance, you might look at User Flows to discover this.

  

  2. Create segments in Matomo : Using Matomo’s segmentation features, you can create a segment that includes users exhibiting these characteristics. For instance :

  • • Segment by page visits : Create a segment that includes users who visited tech product pages or spent time on tech blogs.

  

  • • Segment by event tracking : If you’ve set up event tracking for specific actions (like clicking on “New Tech” category buttons), create a segment based on these events.
    • Combine conditions : Combine various conditions (e.g., pages visited, time spent, specific actions taken) to create a comprehensive segment that accurately represents “Tech Enthusiasts.”

  3. Track and analyse : Apply this segment to your analytics data in Matomo to track and analyse the behaviour of this group separately. Monitor metrics like their conversion rates, time spent on site or specific products they engage with.
  4. Tailor marketing : Use the insights from analysing this segment to tailor marketing strategies. This could involve creating targeted campaigns or customising website content to cater specifically to these users.

  Remember, the key is to define criteria that accurately represent the segment you want to target, use Matomo’s segmentation tools to isolate this group, and effectively derive actionable insights to cater to their preferences or needs.

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  Start for free

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  Track your segmentation efforts 

  Benefits segmentation is a fantastic way to improve your marketing. It can help you deliver a better customer experience, improve your product offering and help your sales reps close more deals. 

  Segmenting your audience with an analytics platform lets you go even deeper. But doing so in a privacy-sensitive way can be difficult. 

  That’s why over 1 million websites choose Matomo as their web analytics solution. Matomo provides exceptional segmentation capabilities while remaining 100% accurate and compliant with global privacy laws.

  Find out how Matomo’s insights can level up your marketing efforts with our 21-day free trial, no credit card required.

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• FFmpeg RTSP drop rate increases when frame rate is reduced

  13 avril 2024, par Avishka Perera

  I need to read an RTSP stream, process the images individually in Python, and then write the images back to an RTSP stream. As the RTSP server, I am using Mediamtx [1]. For streaming, I am using FFmpeg [2].

  


  I have the following code that works perfectly fine. For simplification purposes, I am streaming three generated images.

  


  import time
import numpy as np
import subprocess

width, height = 640, 480
fps = 25
rtsp_server_address = f"rtsp://localhost:8554/mystream"

ffmpeg_cmd = [
    "ffmpeg",
    "-re",
    "-f",
    "rawvideo",
    "-pix_fmt",
    "rgb24",
    "-s",
    f"{width}x{height}",
    "-i",
    "-",
    "-r",
    str(fps),
    "-avoid_negative_ts",
    "make_zero",
    "-vcodec",
    "libx264",
    "-threads",
    "4",
    "-f",
    "rtsp",
    rtsp_server_address,
]
colors = np.array(
    [
        [255, 0, 0],
        [0, 255, 0],
        [0, 0, 255],
    ]
).reshape(3, 1, 1, 3)
images = (np.ones((3, width, height, 3)) * colors).astype(np.uint8)

if __name__ == "__main__":

    process = subprocess.Popen(ffmpeg_cmd, stdin=subprocess.PIPE)
    start = time.time()
    exported = 0
    while True:
        exported += 1
        next_time = start + exported / fps
        now = time.time()
        if next_time > now:
            sleep_dur = next_time - now
            time.sleep(sleep_dur)

        image = images[exported % 3]
        image_bytes = image.tobytes()

        process.stdin.write(image_bytes)
        process.stdin.flush()

    process.stdin.close()
    process.wait()


  


  The issue is, that I need to run this at 10 fps because the processing step is heavy and can only afford 10 fps. Hence, as I reduce the frame rate from 25 to 10, the drop rate increases from 0% to 100%. And after a few iterations, I get a BrokenPipeError: [Errno 32] Broken pipe. Refer to the appendix for the complete log.

  


  As an alternative, I can use OpenCV compiled from source with GStreamer [3], but I prefer using FFmpeg to make the shipping process simple. Since compiling OpenCV from source can be tedious and dependent on the system.

  


  References

  


  [1] Mediamtx (formerly rtsp-simple-server) : https://github.com/bluenviron/mediamtx

  


  [2] FFmpeg : https://github.com/FFmpeg/FFmpeg

  


  [3] Compile OpenCV with GStreamer : https://github.com/bluenviron/mediamtx?tab=readme-ov-file#opencv

  


  Appendix

  


  Creating the source stream

  


  To instantiate the unprocessed stream, I use the following command. This streams the content of my webcam as and RTSP stream.

  


  ffmpeg -video_size 1280x720 -i /dev/video0  -avoid_negative_ts make_zero -vcodec libx264 -r 10 -f rtsp rtsp://localhost:8554/webcam


  


  Error log

  


  ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers&#xA;  built with gcc 12.3.0 (conda-forge gcc 12.3.0-5)&#xA;  configuration: --prefix=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac --cc=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-cc --cxx=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-c&#x2B;&#x2B; --nm=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-nm --ar=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-ar --disable-doc --disable-openssl --enable-demuxer=dash --enable-hardcoded-tables --enable-libfreetype --enable-libharfbuzz --enable-libfontconfig --enable-libopenh264 --enable-libdav1d --enable-gnutls --enable-libmp3lame --enable-libvpx --enable-libass --enable-pthreads --enable-vaapi --enable-libopenvino --enable-gpl --enable-libx264 --enable-libx265 --enable-libaom --enable-libsvtav1 --enable-libxml2 --enable-pic --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libopus --pkg-config=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/pkg-config&#xA;  libavutil      58. 29.100 / 58. 29.100&#xA;  libavcodec     60. 31.102 / 60. 31.102&#xA;  libavformat    60. 16.100 / 60. 16.100&#xA;  libavdevice    60.  3.100 / 60.  3.100&#xA;  libavfilter     9. 12.100 /  9. 12.100&#xA;  libswscale      7.  5.100 /  7.  5.100&#xA;  libswresample   4. 12.100 /  4. 12.100&#xA;  libpostproc    57.  3.100 / 57.  3.100&#xA;Input #0, rawvideo, from &#x27;fd:&#x27;:&#xA;  Duration: N/A, start: 0.000000, bitrate: 184320 kb/s&#xA;  Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 640x480, 184320 kb/s, 25 tbr, 25 tbn&#xA;Stream mapping:&#xA;  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))&#xA;[libx264 @ 0x5e2ef8b01340] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2&#xA;[libx264 @ 0x5e2ef8b01340] profile High 4:4:4 Predictive, level 2.2, 4:4:4, 8-bit&#xA;[libx264 @ 0x5e2ef8b01340] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=4 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=10 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00&#xA;Output #0, rtsp, to &#x27;rtsp://localhost:8554/mystream&#x27;:&#xA;  Metadata:&#xA;    encoder         : Lavf60.16.100&#xA;  Stream #0:0: Video: h264, yuv444p(tv, progressive), 640x480, q=2-31, 10 fps, 90k tbn&#xA;    Metadata:&#xA;      encoder         : Lavc60.31.102 libx264&#xA;    Side data:&#xA;      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A&#xA;[vost#0:0/libx264 @ 0x5e2ef8b01080] Error submitting a packet to the muxer: Broken pipe   &#xA;[out#0/rtsp @ 0x5e2ef8afd780] Error muxing a packet&#xA;[out#0/rtsp @ 0x5e2ef8afd780] video:1kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown&#xA;frame=    1 fps=0.1 q=-1.0 Lsize=N/A time=00:00:04.70 bitrate=N/A dup=0 drop=70 speed=0.389x    &#xA;[libx264 @ 0x5e2ef8b01340] frame I:16    Avg QP: 6.00  size:   147&#xA;[libx264 @ 0x5e2ef8b01340] frame P:17    Avg QP: 9.94  size:   101&#xA;[libx264 @ 0x5e2ef8b01340] frame B:17    Avg QP: 9.94  size:    64&#xA;[libx264 @ 0x5e2ef8b01340] consecutive B-frames: 50.0%  0.0% 42.0%  8.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb I  I16..4: 81.3% 18.7%  0.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb P  I16..4: 52.9%  0.0%  0.0%  P16..4:  0.0%  0.0%  0.0%  0.0%  0.0%    skip:47.1%&#xA;[libx264 @ 0x5e2ef8b01340] mb B  I16..4:  0.0%  5.9%  0.0%  B16..8:  0.1%  0.0%  0.0%  direct: 0.0%  skip:94.0%  L0:56.2% L1:43.8% BI: 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] 8x8 transform intra:15.4% inter:100.0%&#xA;[libx264 @ 0x5e2ef8b01340] coded y,u,v intra: 0.0% 0.0% 0.0% inter: 0.0% 0.0% 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] i16 v,h,dc,p: 97%  0%  3%  0%&#xA;[libx264 @ 0x5e2ef8b01340] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  0%  0% 100%  0%  0%  0%  0%  0%  0%&#xA;[libx264 @ 0x5e2ef8b01340] Weighted P-Frames: Y:52.9% UV:52.9%&#xA;[libx264 @ 0x5e2ef8b01340] ref P L0: 88.9%  0.0%  0.0% 11.1%&#xA;[libx264 @ 0x5e2ef8b01340] kb/s:8.27&#xA;Conversion failed!&#xA;Traceback (most recent call last):&#xA;  File "/home/avishka/projects/read-process-stream/minimal-ffmpeg-error.py", line 58, in <module>&#xA;    process.stdin.write(image_bytes)&#xA;BrokenPipeError: [Errno 32] Broken pipe&#xA;</module>

  


• How to Implement Cross-Channel Analytics : A Guide for Marketers

  17 avril 2024, par Erin

  Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

  You need a cross-channel analytics solution if you answered no to either of these questions. 

  In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

  What is cross-channel analytics ? 

  Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

  The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

  Cross-channel analytics lets you track every channel you use to convert customers, including :

  • Your website
  • Social media profiles
  • Email
  • Paid search
  • E-commerce
  • Retargeting campaigns

  Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

  Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

  That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

  The difference between cross-channel analytics and multi-channel analytics

  Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

  What are the benefits of cross-channel analytics 

  Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

  Get a complete view of the customer journey

  Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

  Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint. 

  

  The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

  In short, you get to see why customers convert so you can learn how to convert more of them.

  Personalise the customer experience

  According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

  When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

  Identify your most effective marketing channels

  Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

  Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

  

  This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Attribute conversions accurately 

  An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

  These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

  Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

  How to set up cross-channel analytics

  Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

  1. Determine your objectives

  Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

  If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

  1. Set relevant KPIs

  You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics. 

  These include :

  • Conversion rate
  • Average visit duration
  • Bounce rate

  1. Implement tracking and analytics tools

  Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

  The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

  1. Set up a multi-touch attribution model

  Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

  There are several attribution models to choose from, including :

  

  Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

  For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

  1. Turn data into insights with reports

  One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

  When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

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  1. Take action

  There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

  But where should you start ?

  Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

  You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

  Cross-channel analytics best practices

  If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

  Use a centralised solution to track everything

  Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

  Think about consumer privacy 

  If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

  You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

  It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

  Monitor data in real time

  So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

  Great, now go further by monitoring the impact of your marketing efforts in real time.

  

  With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

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  Get the web insights you need, without compromising data accuracy.

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  Reallocate marketing budgets based on performance

  When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

  Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

  Cross-channel analytics platforms to get started with 

  The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

  So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

  Matomo

  

  Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

  Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

  Google Analytics

  

  Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

  Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

  Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

  It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

  With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

  Improvado

  

  Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

  While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

  Make the most of cross-channel analytics with Matomo

  Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

  Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

  Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

  Try it free for 21 days. No credit card required.

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