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• Marketing Touchpoints : Examples, KPIs, and Best Practices

  11 mars 2024, par Erin

  The customer journey is rarely straightforward. Rather, each stage comprises numerous points of contact with your brand, known as marketing touchpoints. And each touchpoint is equally important to the customer experience. 

  This article will explore marketing touchpoints in detail, including how to analyse them with attribution models and which KPIs to track. It will also share tips on incorporating these touchpoints into your marketing strategy. 

  What are marketing touchpoints ? 

  Marketing touchpoints are the interactions that take place between brands and customers throughout the latter’s journey, either online or in person. 

  

  By understanding how customers interact with your brand before, during and after a purchase, you can identify the channels that contribute to starting, driving and closing buyer journeys. Not only that, but you’ll also learn how to optimise the customer experience. This can also help you : 

  • Promote customer loyalty through increased customer satisfaction
  • Improve your brand reputation and foster a more positive perception of your brand, supported by social proof 
  • Build brand awareness among prospective customers 
  • Reconnect with current customers to drive repeat business

  According to a 2023 survey, social media and video-sharing platforms are the leading digital touchpoints among US consumers.

  

  With the customer journey divided into three stages — awareness, consideration, and decision — we can group these interactions into three touchpoint segments, depending on whether they occur before, during or after a purchase. 

  Touchpoints before a purchase

  Touchpoints before a purchase are those initial interactions between potential customers and brands that occur during the awareness stage — before they’ve made a purchase decision. 

  Here are some key touchpoints at the pre-purchase stage : 

  • Customer reviews, forums, and testimonials 
  • Social media posts
  • Online ads 
  • Company events and product demos
  • Other digital touchpoints, like video content, blog posts, or infographics
  • Peer referral 

  In PwC’s 2024 Global Consumer Insights Pulse Survey, 54% of consumers listed search engines as their primary source of pre-purchase information, followed by Amazon (35%) and retailer websites (33%). 

  Here are the survey’s findings in Western Europe, specifically : 

  

  Social channels are another major pre-purchase touchpoint ; 25% of social media users aged 18 to 44 have made a purchase through a social media app over the past three months. 

  Touchpoints during a purchase

  Touchpoints during a purchase occur when the prospective customer has made their purchase decision. It’s the beginning of a (hopefully) lasting relationship with them. 

  It’s important to involve both marketing and sales teams here — and to keep track of conversion metrics. 

  Here are the main touchpoints at this stage : 

  • Company website pages 
  • Product pages and catalogues 
  • Communication between customers and sales reps 
  • Product packaging and labelling 
  • Point-of-sale (POS) — the final touchpoint the prospective customer will reach before making the final purchasing decision 

  Touchpoints after a purchase

  You can use touchpoints after a purchase to maintain a positive relationship and keep current customers engaged. Examples of touchpoints that contribute to a good post-purchase experience for the customer include the following : 

  • Thank-you emails 
  • Email newsletters 
  • Customer satisfaction surveys 
  • Cross-selling emails 
  • Renewal options 
  • Customer loyalty programs

  Email marketing remains significant across all touchpoint segments, with 44% of CMOs agreeing that it’s essential to their marketing strategy — and it also plays a particularly important role in the post-purchase experience. For 61.1% of marketing teams, email open rates are higher than 20%.

  

  Sixty-nine percent of consumers say they’ve stopped doing business with a brand following a bad experience, so the importance of customer service touchpoints shouldn’t be overlooked. Live chat, chatbots, self-service resources, and customer service teams are integral to the post-purchase experience.

  Attribution models : Assigning value to marketing touchpoints 

  Determining the most effective touchpoints — those that directly contribute to conversions — is a process known as marketing attribution. The goal here is to identify the specific channels and points of contact with prospective customers that result in revenue for the company.

  

  You can use these insights to understand — and maximise — marketing return on investment (ROI). Otherwise, you risk allocating your budget to the wrong channels. 

  It’s possible to group attribution models into two categories — single-touch and multi-touch — depending on whether you assign value to one or more contributing touchpoints.

  Single-touch attribution models, where you’re giving credit for the conversion to a single touchpoint, include the following :

  • First-touch attribution : This assigns credit for the conversion to the first interaction a customer had with a brand ; however, it fails to consider lower-funnel touchpoints.
  • Last-click attribution : This focuses only on bottom-of-funnel marketing and credits the last interaction the customer had with a brand before completing a purchase.
  • Last non-direct : Credits the touchpoint immediately preceding a direct touchpoint with all the credit.

  Multi-touch attribution models are more complex and distribute the credit for conversion across multiple relevant touchpoints throughout the customer journey :

  • Linear attribution : The simplest multi-touch attribution model assigns equal values to all contributing touchpoints.
  • Position-based or U-shaped attribution : This assigns the greatest value to the first and last touchpoint — with 40% of the conversion credit each — and then divides the remaining 20% across all the other touchpoints.
  • Time-decay attribution : This model assigns the most credit to the customer’s most recent interactions with a brand, assuming that the touchpoints that occur later in the journey have a bigger impact on the conversion.

  Consider the following when choosing the most appropriate attribution model for your business :

  • The length of your typical sales cycle
  • Your marketing goals : increasing awareness, lead generation, driving revenue, etc.
  • How many stages and touchpoints make up your sales funnel

  Sometimes, it even makes sense to measure marketing performance using more than one attribution model.

  With the sheer volume of data that’s constantly generated across numerous online touchpoints, from your website to social media channels, it’s practically impossible to collect and analyse it manually.

  You’ll need an advanced web analytics platform to identify key touchpoints and assign value to them.

  Matomo’s Marketing Attribution feature can accurately measure the performance of different touchpoints to ensure that you’re allocating resources to the right channels. This is done in a compliant manner, without the need of data sampling or requiring cookie consent screens (excluding in Germany and the UK), ensuring both accuracy and privacy compliance.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Customer journey KPIs for measuring marketing campaign performance 

  Measuring the impact of different touchpoints on marketing campaign performance can help you understand how customer interactions drive conversions — and how to optimise your future efforts. 

  

  Clearly, this is not a one-time effort. You should continuously reevaluate the crucial touchpoints that drive the most engagement at different stages of the customer journey. 

  Web analytics platforms can provide valuable insights into ever-changing consumer behaviours and trends and help you make informed decisions. 

  At the moment, Google is the most popular solution in the web analytics industry, with a combined market share of more than 70%. 

  However, if privacy, data accuracy, and GDPR compliance are a priority for you, Matomo is an alternative worth considering. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  KPIs to track before a purchase 

  During the pre-purchase stage, focus on the KPIs that measure the effectiveness of marketing activities across various online touchpoints — landing pages, email campaigns, social channels and ad placement on SERPs, for instance. 

  KPIs to track during the consideration stage include the following : 

  • Cost-per-click (CPC) : The CPC, the total cost of paid online advertising divided by the number of clicks those ads get, indicates whether you’re getting a good ROI. In the UK, the average CPC for search advertising is $1.22. Globally, it averages $0.62.
  • Engagement rate : The engagement rate, which is the total number of interactions divided by the number of followers, is useful for measuring the performance of social media touchpoints. Customer engagement also applies to other channels, like tracking average time on-page, form conversions, bounce rates, and other website interactions. 
  • Click-through rate (CTR) : The CTR — or the number of clicks your ads receive compared to the number of times they’re shown — helps you measure the performance of CTAs, email newsletters and pay-per-click (PPC) advertising.

  KPIs to track during a purchase 

  As a potential customer moves further down the sales funnel and reaches the decision stage, where they’re ready to make the choice to purchase, you should be tracking the following : 

  • Conversion rate : This is the percentage of leads that convert into customers by completing the desired action relative to the total number of website visitors. It shows you whether you’re targeting the right people and providing a frictionless checkout experience.
  • Sales revenue : This refers to the quantity of products sold multiplied by the product’s price. It helps you track the company’s ability to generate profit. 
  • Cost per conversion : This KPI is the total cost of online advertising in relation to the number of conversions. It measures the effectiveness of different marketing channels and the costs of converting prospective customers into buyers. It also forecasts future ad spend.

  KPIs to track after purchase 

  At the post-purchase stage, your priority should be gathering feedback : 

  Customer feedback surveys are great for collecting insights into customers’ post-purchase experience, opinions about your brand, products and services, and needs and expectations. 

  In addition to measuring customer satisfaction, these insights can help you identify points of friction, forecast future growth and revenue and spot customers at risk of churning. 

  Focus on the following customer satisfaction and retention metrics : 

  • Customer Satisfaction Score (CSAT) : This metric, which is gathered through customer satisfaction surveys, helps you gauge satisfaction levels. After all, 77% of consumers consider great customer service an important driver of brand loyalty.
  • Net Promoter Score (NPS) : Based on single-question customer surveys, NPS indicates how likely a customer is to recommend your business.
  • Customer Lifetime Value (CLV) : The CLV is the profit you can expect to generate from one customer throughout their relationship with your company. 
  • Customer Health Score (CHS) : This score can assess how “healthy” the customer’s relationship with your brand is and identify at-risk customers.

  Marketing touchpoints : Tips and best practices 

  Customer experience is more important today than ever. 

  

  Salesforce’s 2022 State of the Connected Consumer report indicated that, for 88% of customers, the experience the brand provides is just as important as the product itself. 

  Here’s how you can build your customer touchpoint strategy and use effective touchpoints to improve customer satisfaction, build a loyal customer base, deliver better digital experiences and drive growth : 

  Understand the customer’s end-to-end experience 

  The typical customer’s journey follows a non-linear path of individual experiences that shape their awareness and brand preference. 

  Seventy-three percent of customers expect brands to understand their needs. So, personalising each interaction and delivering targeted content at different touchpoint segments — supported by customer segmentation and tools like Matomo — should be a priority. 

  Try to put yourself in the prospective customer’s shoes and understand their motivation and needs, focusing on their end-to-end experience rather than individual interactions. 

  Create a customer journey map 

  Once you understand how prospective customers interact with your brand, it becomes easier to map their journey from the pre-purchase stage to the actual purchase and beyond. 

  By creating these visual “roadmaps,” you make sure that you’re delivering the right content on the right channels at the right times and to the right audience — the key to successful marketing.

  Identify best-performing digital touchpoints 

  You can use insights from marketing attribution to pinpoint areas that are performing well. 

  By analysing the data provided by Matomo’s Marketing Attribution feature, you can determine which digital touchpoints are driving the most conversions or engagement, allowing you to focus your resources on optimising these channels for even greater success. 

  This targeted approach helps maximise the effectiveness of your marketing efforts and ensures a higher return on investment.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Discover key marketing touchpoints with Matomo 

  The customer’s journey rarely follows a direct route. If you hope to reach more customers and improve their experience, you’ll need to identify and manage individual marketing touchpoints every step of the way.

  While this process looks different for every business, it’s important to remember that your customers’ experience begins long before they interact with your brand for the first time — and carries on long after they complete the purchase. 

  In order to find these touchpoints and measure their effectiveness across multiple marketing channels, you’ll have to rely on accurate data — and a powerful web analytics tool like Matomo can provide those valuable marketing insights. 

  Try Matomo free for 21-days. No credit card required.

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• "FFmpeg : Error not transitioning to the next song in Discord Bot's queue."

  1er avril 2024, par noober

  I have 3 modules, but I'm sure the error occurs within this module, and here is the entire code within that module :

  


  import asyncio
import discord
from discord import FFmpegOpusAudio, Embed
import os

async def handle_help(message):
    embed = discord.Embed(
        title="Danh sách lệnh cho Bé Mèo",
        description="Dưới đây là các lệnh mà chủ nhân có thể bắt Bé Mèo phục vụ:",
        color=discord.Color.blue()
    )
    embed.add_field(name="!play", value="Phát một bài hát từ YouTube.", inline=False)
    embed.add_field(name="!pause", value="Tạm dừng bài hát đang phát.", inline=False)
    embed.add_field(name="!resume", value="Tiếp tục bài hát đang bị tạm dừng.", inline=False)
    embed.add_field(name="!skip", value="Chuyển đến bài hát tiếp theo trong danh sách chờ.", inline=False)
    embed.add_field(name="!stop", value="Dừng phát nhạc và cho phép Bé Mèo đi ngủ tiếp.", inline=False)
    # Thêm các lệnh khác theo cùng mẫu trên
    await message.channel.send(embed=embed)

class Song:
    def __init__(self, title, player):
        self.title = title  # Lưu trữ tiêu đề bài hát ở đây
        self.player = player

# Thêm đối tượng Song vào hàng đợi
def add_song_to_queue(guild_id, queues, song):
    queues.setdefault(guild_id, []).append(song)

async def handle_list(message, queues):
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    if os.path.exists(log_file_path):
        with open(log_file_path, "r", encoding="utf-8") as f:
            song_list = f.readlines()

        if song_list:
            embed = discord.Embed(
                title="Danh sách bài hát",
                description="Danh sách các bài hát đã phát:",
                color=discord.Color.blue()
            )

            for i, song in enumerate(song_list, start=1):
                if i == 1:
                    song = "- Đang phát: " + song.strip()
                embed.add_field(name=f"Bài hát {i}", value=song, inline=False)

            await message.channel.send(embed=embed)
        else:
            await message.channel.send("Hiện không có dữ liệu trong file log.")
    else:
        await message.channel.send("File log không tồn tại.")

async def handle_commands(message, client, queues, voice_clients, yt_dl_options, ytdl, ffmpeg_options=None, guild_id=None, data=None):
    # Nếu không có ffmpeg_options, sử dụng các thiết lập mặc định
    if ffmpeg_options is None:
        ffmpeg_options = {
            'before_options': '-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 5',
            'options': '-vn -filter:a "volume=0.25"'
        }
    
    # Khởi tạo voice_client
    if guild_id is None:
        guild_id = message.guild.id

    if guild_id in voice_clients:
        voice_client = voice_clients[guild_id]
    else:
        voice_client = None

    # Xử lý lệnh !play
    if message.content.startswith("!play"):
        try:
            # Kiểm tra xem người gửi tin nhắn có đang ở trong kênh voice không
            voice_channel = message.author.voice.channel
            # Kiểm tra xem bot có đang ở trong kênh voice của guild không
            if voice_client and voice_client.is_connected():
                await voice_client.move_to(voice_channel)
            else:
                voice_client = await voice_channel.connect()
                voice_clients[guild_id] = voice_client
        except Exception as e:
            print(e)

        try:
            query = ' '.join(message.content.split()[1:])
            if query.startswith('http'):
                url = query
            else:
                query = 'ytsearch:' + query
                loop = asyncio.get_event_loop()
                data = await loop.run_in_executor(None, lambda: ytdl.extract_info(query, download=False))
                if not data:
                    raise ValueError("Không có dữ liệu trả về từ YouTube.")
                url = data['entries'][0]['url']

            player = FFmpegOpusAudio(url, **ffmpeg_options)
            # Lấy thông tin của bài hát mới đang được yêu cầu
            title = data['entries'][0]['title']
            duration = data['entries'][0]['duration']
            creator = data['entries'][0]['creator'] if 'creator' in data['entries'][0] else "Unknown"
            requester = message.author.nick if message.author.nick else message.author.name
                    
            # Tạo embed để thông báo thông tin bài hát mới
            embed = discord.Embed(
                title="Thông tin bài hát mới",
                description=f"**Bài hát:** *{title}*\n**Thời lượng:** *{duration}*\n**Tác giả:** *{creator}*\n**Người yêu cầu:** *{requester}*",
                color=discord.Color.green()
            )
            await message.channel.send(embed=embed)
            
            # Sau khi lấy thông tin của bài hát diễn ra, gọi hàm log_song_title với title của bài hát
            # Ví dụ:
            title = data['entries'][0]['title']
            await log_song_title(title)

            # Thêm vào danh sách chờ nếu có bài hát đang phát
            if voice_client.is_playing():
                queues.setdefault(guild_id, []).append(player)
            else:
                voice_client.play(player)
                
        except Exception as e:
            print(e)
            
    if message.content.startswith("!link"):
            try:
                voice_client = await message.author.voice.channel.connect()
                voice_clients[voice_client.guild.id] = voice_client
            except Exception as e:
                print(e)

            try:
                url = message.content.split()[1]

                loop = asyncio.get_event_loop()
                data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=False))

                song = data['url']
                player = discord.FFmpegOpusAudio(song, **ffmpeg_options)

                voice_clients[message.guild.id].play(player)
            except Exception as e:
                print(e)

    # Xử lý lệnh !queue
    elif message.content.startswith("!queue"):
        queue = queues.get(guild_id, [])
        if queue:
            await message.channel.send("Danh sách chờ:")
            for index, item in enumerate(queue, 1):
                await message.channel.send(f"{index}. {item.title}")
        else:
            await message.channel.send("Không có bài hát nào trong danh sách chờ.")

    # Xử lý lệnh !skip
    elif message.content.startswith("!skip"):
        try:
            if voice_client and voice_client.is_playing():
                voice_client.stop()
                await play_next_song(guild_id, queues, voice_client, skip=True)
                await remove_first_line_from_log()
        except Exception as e:
            print(e)

    # Xử lý các lệnh như !pause, !resume, !stop
    elif message.content.startswith("!pause"):
        try:
            if voice_client and voice_client.is_playing():
                voice_client.pause()
        except Exception as e:
            print(e)

    elif message.content.startswith("!resume"):
        try:
            if voice_client and not voice_client.is_playing():
                voice_client.resume()
        except Exception as e:
            print(e)

    elif message.content.startswith("!stop"):
        try:
            if voice_client:
                voice_client.stop()
                await voice_client.disconnect()
                del voice_clients[guild_id]  # Xóa voice_client sau khi dừng
        except Exception as e:
            print(e)

async def log_song_title(title):
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        # Kiểm tra xem tệp tin log đã tồn tại chưa
        if not os.path.exists(log_file_path):
            # Nếu chưa tồn tại, tạo tệp tin mới và ghi title vào tệp tin đó
            with open(log_file_path, 'w', encoding='utf-8') as file:
                file.write(title + '\n')
        else:
            # Nếu tệp tin log đã tồn tại, mở tệp tin và chèn title vào cuối tệp tin
            with open(log_file_path, 'a', encoding='utf-8') as file:
                file.write(title + '\n')
    except Exception as e:
        print(f"Error logging song title: {e}")

async def remove_first_line_from_log():
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        with open(log_file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()
        # Xóa dòng đầu tiên trong list lines
        lines = lines[1:]
        with open(log_file_path, "w", encoding="utf-8") as f:
            for line in lines:
                f.write(line)
    except Exception as e:
        print(f"Error removing first line from log: {e}")
        
async def clear_log_file():
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        with open(log_file_path, "w", encoding="utf-8") as f:
            f.truncate(0)
    except Exception as e:
        print(f"Error clearing log file: {e}")


async def play_next_song(guild_id, queues, voice_client, skip=False):
    queue = queues.get(guild_id, [])
    if queue:
        player = queue.pop(0)
        voice_client.play(player, after=lambda e: asyncio.run_coroutine_threadsafe(play_next_song(guild_id, queues, voice_client, skip=False), voice_client.loop))
        if skip:
            return
        else:
            await remove_first_line_from_log()  # Xóa dòng đầu tiên trong file log
    elif skip:
        await remove_first_line_from_log()  # Xóa dòng đầu tiên trong file log
        await voice_client.disconnect()
        del voice_client[guild_id]  # Xóa voice_client sau khi dừng
    else:
        await clear_log_file()  # Xóa dòng đầu tiên trong file log
        await voice_client.disconnect()
        del voice_client[guild_id]  # Xóa voice_client sau khi dừng


  


  I have tried asking ChatGPT, Gemini, or Bing, and they always lead me into a loop of errors that cannot be resolved. This error only occurs when the song naturally finishes playing due to its duration. If the song is playing and I use the command !skip, the next song in the queue will play and function normally. I noticed that it seems like if a song ends naturally, the song queue is also cleared immediately. I hope someone can help me with this

  


• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

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