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• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• ffmpeg drawtext filter not found [closed]

  3 octobre 2023, par Sonja

  I have been trying to add frame numbers to my video using ffmpeg :

  


  ./ffmpeg -i VideoFileNameIN.mp4 -crf 10 -vf "drawtext=fontfile=/Library/Fonts/Arial.ttf: text='%{frame_num}': x=(w-tw)/2: y=h-(2*lh): fontcolor=black: fontsize=20: box=1: boxcolor=white: boxborderw=5" -c:a copy VideoFileNameOUT.mp4


  


  ...but when I run it it says :

  


  No such filter: 'drawtext' 
Error initializing a simple filtergraph
Error opening output file
Error opening output files: Filter not found


  


  All my files are in the downloads folder.
Any suggestions ?

  


• arm : vp9 : Add NEON loop filters

  10 octobre 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).
  Signed-off-by : Martin Storsjö <martin@martin.st>
  

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