"Activation channel" typically refers to the specific marketing or communication channel through which a company or organisation engages with its audience to prompt a desired action or response. The term is often used in the context of customer acquisition and engagement strategies.
Activation channels can vary widely and may include:
- Email Marketing: Sending targeted emails encourages users to take specific actions, such as signing up, purchasing, or completing a registration.
- Social Media: Leveraging social platforms to engage with an audience and guide them toward desired actions, like following, sharing, or purchasing.
- In-App Notifications: Using notifications within mobile apps to prompt users to complete actions, subscribe, or engage with the app's features.
- Online Advertising: Running targeted ads on various digital platforms to attract users and lead them to specific landing pages or calls to action.
- SMS Marketing: Send text messages to inform users of offers, promotions, or events and encourage them to participate.
- Content Marketing: Creating and distributing valuable content to inform and engage users and lead them to desired actions.
- Referral Programs: Use word-of-mouth marketing to encourage current customers to refer friends and family.
The choice of activation channel depends on the target audience, the nature of the desired action, and the goals of the marketing or engagement campaign. Companies often use a combination of activation channels to reach their audience through various touchpoints and encourage them to take specific actions or become more engaged with the brand or product.
"API-first" is a development and design approach that prioritises the creation and exposure of Application Programming Interfaces (APIs) as a foundational component of a software project. In an API-first approach, APIs are not merely an afterthought or an interface to an existing application; rather, they are designed and developed as a central element from the beginning of the project. This approach emphasises creating well-structured, documented, and versatile APIs enabling communication and data exchange between software components, services, or systems.
# Bounce Rate
"Bounce Rate" is a web analytics metric that measures the percentage of visitors to a website who navigate away from the site after viewing only one page without taking any further action, such as clicking on links, interacting with the page, or visiting other pages on the site. In other words, a high bounce rate indicates that many visitors left the website without engaging further.
A high bounce rate can be a sign of various issues, including:
1. Irrelevant Content: Visitors may not find the content or information they sought on the landing page.
2. Slow Loading: Slow-loading pages can lead to frustration and cause users to leave quickly.
3. Poor User Experience: Confusing navigation, unattractive design, or unclear calls to action can deter further engagement.
4. Misleading Ad Campaigns: Visitors may leave if the landing page doesn't align with the expectations set by an ad or link.
5. Technical Issues: Errors or broken links can result in a high bounce rate.
Bricks and Mortar
"Bricks and mortar" (sometimes written as "brick and mortar") is a term that refers to traditional, physical, and tangible businesses with a physical presence, such as retail stores, offices, or other physical locations. It is often used in contrast to online or e-commerce businesses that operate exclusively in the digital realm.
"CTR" stands for "Click-Through Rate," a widely used metric in online advertising and digital marketing. CTR measures the effectiveness of an online advertising campaign or a specific link in terms of the percentage of users who click on it, relative to the total number of users who view the advertisement or link (impressions).
The formula for calculating Click-Through Rate (CTR) is:
CTR = (Number of Clicks / Number of Impressions) × 100
CTR is expressed as a percentage, providing insight into how compelling and engaging an ad or link is to the audience. A higher CTR indicates that a larger proportion of users who saw the ad or link took the desired action, typically clicking on the ad to learn more or visiting a website. CTR is often used to measure the success of online advertising campaigns and to optimise ad content and placement to improve the effectiveness of marketing efforts.
Digital promotions refer to marketing and advertising initiatives conducted through online and digital channels to promote products, services, or brands. These promotions leverage digital platforms, such as websites, social media, email marketing, search engines, and mobile apps, to reach and engage with a target audience. They often encompass a variety of techniques and strategies, including discounts, coupons, contests, giveaways, and special offers.
Digital promotions are designed to attract and engage potential customers, drive online traffic, boost conversions, and ultimately increase sales or brand awareness. They are typically highly measurable, allowing businesses to track their effectiveness and adjust their strategies accordingly. Additionally, digital promotions often leverage data analytics and customer profiling to personalize offers and messages, making them more relevant and appealing to the intended audience.
Drive to Store
"Drive-to-store" is a marketing strategy or campaign designed to encourage and motivate consumers to visit a physical retail location, such as a brick-and-mortar store, restaurant, or dealership. This strategy leverages digital and traditional marketing channels to create awareness, generate interest, and ultimately drive foot traffic to a physical business location. The goal is to convert online or offline interactions into in-person visits, where customers can purchase, experience products firsthand, or engage with the brand directly. Drive-to-store marketing often employs tactics like targeted advertising, location-based promotions, and mobile apps to guide and entice consumers to visit a specific store or location.
# First-party data
"First-party data" refers to data collected by a company or organisation directly from its own customers or users. This data is obtained through customer interactions on the company's platforms and channels, such as its website, mobile apps, customer surveys, or point-of-sale systems. First-party data is considered highly valuable because it is specific to a company's audience and comes directly from those with a relationship with the business.
Examples of first-party data include:
1. Customer purchase history: Data on products or services purchased by customers.
2. Website analytics: Information on user behaviour and engagement on the company's website.
3. User account information: Data users provide when they create accounts or profiles on the company's platforms.
4. Email subscriptions: Information about users who have subscribed to newsletters or updates.
5. Customer feedback: Data from surveys, reviews, and customer support interactions.
First-party data is often used for various purposes, including improving customer experiences, personalising marketing efforts, targeting advertising, and making data-driven business decisions. It is particularly valuable because it is typically considered more reliable and accurate compared to third-party data (data acquired from external sources), and it allows businesses to have greater control over how the data is collected, used, and protected.
"Geotargeting" is a digital marketing and advertising strategy that involves delivering content or advertisements to a specific audience based on their geographic location. This strategy uses a user's IP address, GPS data from mobile devices, or other location information to determine their approximate physical location and tailor marketing messages, content, or offers relevant to that specific location.
Geotargeting can be used in various ways, such as:
1. **Local Advertising:** Displaying ads for nearby businesses or services to users based on location. For example, showing restaurant ads to users in the same city or ads for a retail store near their current location.
2. **Personalised Content:** Customizing website content, product recommendations, or promotions based on the user's location. For instance, displaying different homepage content for users in different cities.
3. **Local SEO:** Optimizing website content to appear in search results for local keywords, helping businesses attract customers searching for nearby products or services.
4. **Location-Based Offers:** Providing special discounts or promotions to users when they are near a physical store, encouraging them to visit and make a purchase.
5. **Local Social Media Targeting:** Running social media ads targeted at users in specific geographic regions.
Geotargeting is valuable for businesses that want to reach a local or regional audience, as it allows them to deliver highly relevant and contextually appropriate content to users based on their location. This can lead to more effective and engaging marketing campaigns and increased foot traffic to physical locations.
"Hyper-personalisation" is an advanced marketing and customer engagement strategy that goes beyond traditional personalisation by leveraging extensive data and technology to create highly individualised and tailored experiences for each customer. It aims to deliver content, product recommendations, and interactions that are uniquely relevant to a specific user based on their behaviours, preferences, and demographics.
Key aspects of hyper-personalization include:
1. **Data Utilisation:** Extensive data sources, such as browsing history, purchase history, location data, and social media activity, are used to build a comprehensive customer profile.
2. **Real-time Adaptation:** Content and recommendations are adjusted in real-time to respond to a user's immediate actions or context.
3. **AI and Machine Learning:** Advanced algorithms and machine learning models are employed to predict user behaviour and tailor the user experience accordingly.
4. **Omnichannel Integration:** Personalisation extends across all customer touchpoints, from websites and mobile apps to email, social media, and physical stores.
5. **Content Customisation:** Content, product suggestions, and messaging are tailored to individual preferences, increasing the likelihood of engagement and conversion.
A "loyalty programme" is a structured marketing strategy businesses and organisations implement to incentivise and reward customers for their repeat or continued engagement with their products or services. Loyalty programs are designed to foster customer loyalty, encouraging them to make repeat purchases and remain loyal to the brand. These programs typically offer participants various rewards, incentives, or exclusive benefits.
Key features of a loyalty program often include:
1. Rewards: Participants earn points, discounts, cashback, or other incentives for purchasing or engaging with the brand.
2. Tiers or Levels: Many loyalty programs have different tiers or levels that offer increasing benefits as customers reach higher levels by accumulating more points or making more purchases.
3. Exclusive Offers: Loyalty program members may receive exclusive access to special promotions, sales, or products unavailable to the general public.
4. Personalisation: Programs often use customer data to tailor offers and rewards to individual preferences and behaviours.
5. Communication: Members are typically informed about promotions and rewards through email, mobile apps, or other communication channels.
6. Data Collection: Loyalty programs collect valuable customer data, which can help businesses understand customer behaviour and preferences.
7. Long-term Engagement: The primary goal is to create a long-term relationship with customers and keep them coming back.
Loyalty programs can take various forms, including point-based systems, tiered memberships, cashback incentives, and more. They are widely used in retail, hospitality, airlines, and e-commerce industries to build and maintain a loyal customer base.
Machine learning is a technology that is crucial in enhancing the customer experience and optimising operations. It uses algorithms and models to analyse customer data, predict shopping behaviour, and make personalised recommendations. For example, machine learning can power product recommendation engines, analyse shopping patterns, and segment customers for targeted marketing campaigns. It also helps retailers optimise inventory management, pricing strategies, and fraud detection. In e-commerce, machine learning is leveraged to provide a seamless and tailored shopping experience, increasing sales and improving overall efficiency in the digital marketplace.
"Omnichannel" describes a strategy providing a seamless and integrated customer experience across various channels and touchpoints. These channels can include online and offline platforms, such as websites, mobile apps, social media, email, telephone, physical stores, and more.
An omnichannel approach aims to ensure that customers can interact with a business or brand consistently and efficiently, regardless of the channels they use. This means that customers can start interacting on one channel, such as browsing products on a mobile app, and then seamlessly transition to another channel, like a physical store or a website, with the same level of service and access to their information.
"Redemption" refers to the act of using a coupon, voucher, reward, or special offer to take advantage of a discount, promotion, or benefit. It is when a customer or user exchanges a promotional offer or incentive for goods or services.
For example, when a customer uses a coupon code to get a discount on an online purchase, that action is considered redemption. Similarly, redeeming loyalty points for a free product, claiming a gift card, or cashing in a rebate are all forms of redemption.
Redemption is an important aspect of marketing and loyalty programs because it reflects how successful a promotion or incentive has been in encouraging customers to take a desired action, such as making a purchase or engaging with a brand. Analysing redemption rates helps businesses assess the effectiveness of their marketing efforts and adjust their strategies accordingly.
"ROAS" stands for "Return on Ad Spend." It is a metric used in digital advertising and marketing to measure advertising campaigns' effectiveness and efficiency, particularly in revenue generation. ROAS is calculated by dividing the revenue generated from an advertising campaign by the cost of that campaign. The result is typically expressed as a ratio or percentage.
Mathematically, the ROAS formula is:
ROAS = (Revenue from Ad Campaign) / (Cost of Ad Campaign)
A ROAS value greater than 1 indicates that the campaign generates more revenue than the cost, generally considered a positive return on investment. For example, a ROAS of 2 means that for every **€1** spent on advertising, the campaign generated **€**2 in revenue. Advertisers often use ROAS to evaluate the performance of specific advertising channels, campaigns, or keywords, allowing them to allocate their advertising budget more effectively and optimise their marketing strategies.
"RPV" stands for "Revenue per Visitor," a key performance metric in online businesses and e-commerce. RPV measures the average revenue generated for each visitor to a website or online platform. It is a crucial metric for evaluating the effectiveness of a website or online marketing efforts in terms of generating income.
The formula for calculating Revenue per Visitor (RPV) is:
RPV = Total Revenue / Total Number of Visitors
This metric helps businesses understand how effectively they are monetising their web traffic. A higher RPV indicates that a website or online platform is generating more revenue for each visitor, which can result from factors such as effective marketing, user engagement, and optimised conversion processes.
By monitoring RPV, businesses can assess the impact of various changes, such as website design, product offerings, or marketing campaigns, and make data-driven decisions to improve their online revenue-generating strategies.
"segmentation" refers to the practice of dividing a broader target market into smaller, more homogenous groups or segments based on shared characteristics, needs, or behaviours. Segmentation aims to understand better and cater to the diverse preferences and requirements of different customer groups. By dividing the market into segments, businesses can develop more focused and effective marketing strategies, products, and messaging for each segment, ultimately enhancing their chances of reaching and appealing to their audience.
Segmentation variables can include demographic factors (such as age, gender, income, and location), psychographic factors (like lifestyle, values, and interests), behavioural factors (such as purchasing history, usage patterns, and brand loyalty), and more. Each segment is treated as a distinct target audience, allowing marketers to tailor their marketing efforts to that group's specific characteristics and needs, which often leads to more successful and relevant marketing campaigns.
In a retail context, "transactional data" refers to detailed information about individual sales and purchases made by customers. It includes data elements such as product or service items, quantities, prices, payment methods, and timestamps. Transactional data is crucial for retailers as it helps in tracking sales, managing inventory, analyzing customer behavior, and making informed business decisions. Retailers often use transactional data for various purposes, including optimizing product offerings, pricing strategies, and marketing campaigns, as well as understanding customer preferences and forecasting demand. This data can be collected through point-of-sale (POS) systems, e-commerce platforms, and various payment processing tools.
Zero-party data refers to the data that individuals willingly and proactively share with businesses or organisations. Unlike first-party data (collected by a company about its customers) or third-party data (acquired from external sources), the data subject explicitly and intentionally provides zero-party data, often in response to a direct request or inquiry.
Zero-party data can include preferences, interests, feedback, survey responses, and other information individuals share voluntarily. This data is considered valuable because it offers direct insights into a person's intentions, choices, and desires, making it highly relevant for personalised marketing and customer engagement. It can help businesses build stronger, trust-based relationships with their customers by using their explicit input to tailor products, services, and experiences to individual preferences.