1.Optimization of the channel – The key insights that businesses have gathered about their customers over the years have primarily been their age, location, and gender. These details provide businesses and marketers with very little information about who their clients are and what they want.
Data science, in the form of tools like affinity (or market basket) analysis, can paint a much more accurate picture of the type of person a company wants to appeal to and where they should best market to them.
A detailed analysis of the customer’s social media interaction can be used to make connections that will form a specific story or pathway. This path will highlight any missed opportunities on YouTube, Instagram, Pinterest, or any other channel where advertising and content would be most effective with your ideal client.
2. Segmentation Of Customers –Recognizing your customers’ various needs is critical to a successful marketing strategy. Although no two customers are alike, their pain points, desires, and aspirations can be grouped in useful ways to inform marketing strategies and drive conversions.
Customers can be segmented based on attributes like their location, previous purchase patterns, and how they navigated your website. Data scientists can use machine learning algorithms to determine the potential value of each ideal customer group as well as which products are likely to appeal to them. This can then be used to guide your content strategy, channel optimization, and advanced lead targeting.
3. Advanced Lead Scoring And Lead Targeting – Reaching out to the right potential customers at the right time is widely acknowledged as the most difficult aspect of the digital marketing process. Data science and machine learning systems can greatly simplify customer analytics.
Data scientists can predict which offers or products will be most appealing to different customers and demographics at different times based on extensive analysis of your collected marketing data and insights from data libraries.
The potential value of each lead and potential customer can be scored based on factors such as previous behaviour of similar customers, word choice when interacting with you, and customer segment characteristics.
The procedure will prevent you from wasting money on guesswork and trial and error. The effectiveness of each predictive algorithm will then be used to inform future marketing decisions, including the creation of any new products or services. Using a machine learning model to target leads will transform this process into a streamlined end-to-end solution that is constantly refining and improving itself.
4. Interaction And Analytics In Real Time – There are advantages to using data collected over time to inform marketing decisions, but delays in gathering this data can put businesses at a disadvantage.
Real-time analytics allow businesses to measure and process customer behaviour as it occurs, providing meaningful, actionable insights at a critical point in the customer conversion process. Real-time analytics also allow for a quicker response time when your target market fluctuates, saving you money and marketing time in the long run.
Real-time analytics can be used in marketing in two ways: Sending targeted offers and incentives to appropriate customers in-store or on your website; and analysing customer behaviour to determine when and why sales are lost or made.
5. Content Strategy – Creating an effective content marketing strategy to attract new leads can feel like a shot in the dark at times.
Even if your content receives a high response and conversion rate, it can feel nearly impossible to pinpoint what exactly your customers are enjoying without data and analytics to back up your findings.
This is where data science comes into play. Though testing is still required to truly understand the quality of your content, methods such as serial testing enable you to do so in the most efficient and time-efficient manner possible, by utilising an unsupervised machine learning algorithm.
Serial testing can help you focus on small details like word choice and colour. Time-series forecasting techniques can then help you predict when these creative choices will be most effective across all platforms, allowing you to put fully optimised content in front of the exact right people at the exact right time.
6. Conduct A Sentiment Analysis –It is critical to establish a positive reputation for your company or brand. Even before they have used your service, your customer’s initial reaction when they discover your social media channels or website can have a significant impact on how positively they perceive you. This reaction is frequently influenced by reviews or responses left by others.
Using sentiment analysis to tap into your clients’ emotions is an important way to ensure you have control over your reputation. Though this can be done manually, machine learning algorithms greatly speed up and improve the effectiveness of this data analysis.
Individual words can be assigned specific values (negative, neutral, or positive) to give each social media post a score based on the reactions in the comments section. Using speech-to-text analytics, the same theory can be applied to email correspondence, Google reviews, and even phone conversations
This can assist you in identifying the products, services, or social media marketing efforts that elicit the desired reactions from your pool of potential customers, as well as determining where any customer service breakdowns are occurring.
7. Keeping Customer Loyalty –Maintaining customer loyalty and increasing the lifetime value of your average customer may be a more profitable use of your marketing budget than acquiring new customers. Data science and machine learning models can assist businesses in identifying three factors that may contribute to increased customer loyalty: the next best action or offer for each customer as they interact with your website or product; how a customer might react in a specific situation; and what the issue is if a client does not return
Once these three factors are identified, you can solve many problems that may be preventing customers from returning, set up automatic recommendations for clients who have previously worked with you, and predict the best set of actions should a specific interaction occur.
Marketing to a customer who has already enjoyed their experience with you is easier and less expensive than marketing to a ‘cold’ lead due to the availability of their data.
8. Predictive Analytics – Predictive analytics uses machine learning models (and sometimes artificial intelligence in general) to forecast what will happen in specific situations that affect your business or your customers.
With more Internet of Things (IoT) devices than ever before, there has never been more data to base these predictions on, and thus, by extension, the predictions that are possible with the right system in place have never been more accurate.
9.Recommender Systems – A successful predictive analytics system serves as the foundation for recommendation engines. A recommendation engine can be of two types: Content-based filtering and collaborative filtering
A collaborative filtering-based recommendation engine will recommend products to customers based on the purchasing habits of other customers, regardless of the product type. A content-based filtering recommendation engine is more aware of product type and description and recommends products that are similar to one another.
Both types of recommendation engines have drawbacks. Collaborative may recommend products that do not meet the customer’s exact needs at the time, whereas content-based filters may recommend very similar products rather than up-selling.
As a result, most businesses use a combination of the two, recommending products based on type, description, and past popularity with similar customers.
10. Marketing Budget Optimization – The overall goal of data science is to ensure that every penny of your company’s marketing budget is spent wisely and profitably. Your company can save money by optimising who and when you market specific products to.
All of the examples above can help you achieve this, allowing you to put together a sharp, comprehensive, and often automated marketing plan that covers everything from identifying your customer base to determining how the weather might affect the sale of one specific product.
