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• Marketing Cohort Analysis : How To Do It (With Examples)

  12 janvier 2024, par Erin

  The better you understand your customers, the more effective your marketing will become. 

  The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

  A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

  In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

  What is cohort analysis in marketing ?

  A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

  These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

  It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

  You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

  

  There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

  Acquisition-based cohort analysis

  An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

  For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

  You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

  Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

  As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

  Behavioural-based cohort analysis

  A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

  

  Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

  What can you achieve with a marketing cohort analysis ?

  A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

  

  Understand which customers churn and why

  Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

  Learn which customers are most valuable

  Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

  For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

  Discover how to improve your product

  You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

  Find out how to improve your marketing campaign

  A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

  If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

  Measure the impact of changes

  You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

  If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

  The problem with cohort analysis in Google Analytics

  Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues. 

  For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

  In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

  As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

  Below, we’ll show how you can run a marketing cohort analysis using Matomo.

  Set a goal

  Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

  For example, you may want to improve your customer retention rate over the first 90 days. 

  Define cohorts

  Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

  Matomo makes it easy to define cohorts and create charts. 

  In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

  

  In the example above, we’ve created cohorts by bounce rate. 

  You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

  • Unique visitors
  • Return visitors
  • Conversion rates
  • Revenue
  • Actions per visit

  Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

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  Analyse your cohort chart

  Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

  Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

  

  Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

  The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

  For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

  Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

  Segment your cohort chart

  Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

  

  

  Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

  Start using Matomo for marketing cohort analysis

  A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

  Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

  Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

  Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

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• arm : vp9 : Add NEON loop filters

  14 novembre 2016, par Martin Storsjö

  arm : vp9 : Add NEON loop filters
  This work is sponsored by, and copyright, Google.
  The implementation tries to have smart handling of cases
  
  where no pixels need the full filtering for the 8/16 width
  
  filters, skipping both calculation and writeback of the
  
  unmodified pixels in those cases. The actual effect of this
  
  is hard to test with checkasm though, since it tests the
  
  full filtering, and the benefit depends on how many filtered
  
  blocks use the shortcut.
  Examples of relative speedup compared to the C version, from checkasm :
  
                            Cortex       A7     A8     A9    A53
  
  vp9_loop_filter_h_4_8_neon :          2.72   2.68   1.78   3.15
  
  vp9_loop_filter_h_8_8_neon :          2.36   2.38   1.70   2.91
  
  vp9_loop_filter_h_16_8_neon :         1.80   1.89   1.45   2.01
  
  vp9_loop_filter_h_16_16_neon :        2.81   2.78   2.18   3.16
  
  vp9_loop_filter_mix2_h_44_16_neon :   2.65   2.67   1.93   3.05
  
  vp9_loop_filter_mix2_h_48_16_neon :   2.46   2.38   1.81   2.85
  
  vp9_loop_filter_mix2_h_84_16_neon :   2.50   2.41   1.73   2.85
  
  vp9_loop_filter_mix2_h_88_16_neon :   2.77   2.66   1.96   3.23
  
  vp9_loop_filter_mix2_v_44_16_neon :   4.28   4.46   3.22   5.70
  
  vp9_loop_filter_mix2_v_48_16_neon :   3.92   4.00   3.03   5.19
  
  vp9_loop_filter_mix2_v_84_16_neon :   3.97   4.31   2.98   5.33
  
  vp9_loop_filter_mix2_v_88_16_neon :   3.91   4.19   3.06   5.18
  
  vp9_loop_filter_v_4_8_neon :          4.53   4.47   3.31   6.05
  
  vp9_loop_filter_v_8_8_neon :          3.58   3.99   2.92   5.17
  
  vp9_loop_filter_v_16_8_neon :         3.40   3.50   2.81   4.68
  
  vp9_loop_filter_v_16_16_neon :        4.66   4.41   3.74   6.02
  The speedup vs C code is around 2-6x. The numbers are quite
  
  inconclusive though, since the checkasm test runs multiple filterings
  
  on top of each other, so later rounds might end up with different
  
  codepaths (different decisions on which filter to apply, based
  
  on input pixel differences). Disabling the early-exit in the asm
  
  doesn’t give a fair comparison either though, since the C code
  
  only does the necessary calcuations for each row.
  Based on START_TIMER/STOP_TIMER wrapping around a few individual
  
  functions, the speedup vs C code is around 4-9x.
  This is pretty similar in runtime to the corresponding routines
  
  in libvpx. (This is comparing vpx_lpf_vertical_16_neon,
  
  vpx_lpf_horizontal_edge_8_neon and vpx_lpf_horizontal_edge_16_neon
  
  to vp9_loop_filter_h_16_8_neon, vp9_loop_filter_v_16_8_neon
  
  and vp9_loop_filter_v_16_16_neon - note that the naming of horizonal
  
  and vertical is flipped between the libraries.)
  In order to have stable, comparable numbers, the early exits in both
  
  asm versions were disabled, forcing the full filtering codepath.
                             Cortex           A7      A8      A9     A53
  
  vp9_loop_filter_h_16_8_neon :             597.2   472.0   482.4   415.0
  
  libvpx vpx_lpf_vertical_16_neon :         626.0   464.5   470.7   445.0
  
  vp9_loop_filter_v_16_8_neon :             500.2   422.5   429.7   295.0
  
  libvpx vpx_lpf_horizontal_edge_8_neon :   586.5   414.5   415.6   383.2
  
  vp9_loop_filter_v_16_16_neon :            905.0   784.7   791.5   546.0
  
  libvpx vpx_lpf_horizontal_edge_16_neon : 1060.2   751.7   743.5   685.2
  Our version is consistently faster on on A7 and A53, marginally slower on
  
  A8, and sometimes faster, sometimes slower on A9 (marginally slower in all
  
  three tests in this particular test run).
  This is an adapted cherry-pick from libav commit
  
  dd299a2d6d4d1af9528ed35a8131c35946be5973.
  Signed-off-by : Ronald S. Bultje <rsbultje@gmail.com>
  

  • [DH] libavcodec/arm/Makefile
  • [DH] libavcodec/arm/vp9dsp_init_arm.c
  • [DH] libavcodec/arm/vp9lpf_neon.S

• Death of A Micro Center

  21 septembre 2012, par Multimedia Mike — History

  The Micro Center computer store located in Santa Clara, CA, USA closed recently :

  
  
  

  I liked Micro Center. I have liked Micro Center ever since I first visited their Denver, CO location 10 years ago. I would sometimes drive an hour in each direction just to visit that shop. I was excited to see that they had a location in the Bay Area when I moved here a few years ago (despite the preponderance of Fry’s stores).

  Now this location is gone. I wonder how much of the “we couldn’t come to favorable terms on a lease” was true (vs. an excuse to close a retail store at a time when more business is moving online, particularly in the heart of Silicon Valley). But that’s not what I wanted to discuss. I came here to discuss…

  The Micro Center Window Logos

  The craziest part about shopping the Santa Clara Micro Center location was the logos they displayed on the window outside. Every time I saw it, it made me sentimental for a time when some of these logos were current, or when some of these companies were still in business. Some of the logos on their front window were for companies I’ve never heard of. It reminds me of the nearby 7-11 convenience stores when I was growing up– their walls were decorated with people sporting embarrassingly 1970s styles long after the 1970s had transpired.

  I thought I would record what those front window logos were and try to pinpoint when the store launched exactly (assuming the logos have been their since the initial opening and never changed).

  
  
  Click for larger image
  

  Here we have Lotus, Hewlett Packard/HP, Corel, Fuji, Power Macintosh, NEC, and Fujitsu. Lotus was purchased by IBM in 1995 and still seems to be maintained as a separate brand. The Power Macintosh was introduced as a brand in 1994. Corel’s logo has seen a few mutations over the years but I don’t know when this one fell out of favor.

  Fuji (vs. Fujitsu) appears to refer to Fujifilm, though this logo is also obsolete.

  
  
  Click for larger image
  

  Hayes– I specifically remember reading the Slashdot post accouncing that Hayes is dead (followed by many comments reminiscing about the Hayes command set). Here is the post, from early 1999.

  From Googling, it doesn’t appear IBM still has a presence in the consumer computing space (though they do have something pertaining to software for consumer products). Then there’s the good old rainbow Apple logo, something that went away in 1997. I suspect 1997 was also the last hurrah of the name ‘Macintosh’ (though I remember mistakenly referring to Apple computer products as Macintoshes well into the mid-2000s and inadvertently angering some Apple enthusiasts).

  
  
  Click for larger image
  

  As for the next segment, obviously, both Sony and Toshiba are still very much alive. Iomega was acquired by EMC in 2008 but is still maintained as a separate brand. USRobotics is still around and making — what else ? — 56K modems (and their current logo is slightly different than the one seen here).

  Targus seems to be a case maker (“Leading Provider of Cases, Bags and Accessories for Laptops and Tablets”). I wonder if that’s just their current business or if they had more areas long ago ? It seems strange that they would get brand billing like this.

  Finally, searching for information about Practical Peripherals only produces sites about how they’re long dead (like this history lesson). It’s unclear when they died.

  The interior of this store was also decorated with more technology company logos near the ceiling (I didn’t really register that fact until I had visited many times). Regrettably, I now won’t be able to see how up to date those logos were.

  Based on the data points above, it’s safe to conclude that the store opened between 1995 or 1996 (again, assuming the logos were placed at opening and never changed).

  Epilogue

  Here’s one more curious item still visible from the outside :

  
  
  

  “See the world’s fastest PC !” Featuring an Intel Core 2 Extreme ? That CPU dates back to 2007 and was succeeded by Nehalem in late 2008. So even that sign, which is presumably easier and cleaner to replace than the window logos, was absurdly out of date.