Why is it Important to Integrate Analytics to Restaurant Management?

Today, restaurant data comes in from various sources. They often struggle to identify issues that will have a major impact on their food chains. Additionally, many individual restaurants that are part of countrywide chains, often end up being managed as standalone entities. As a result, it has become inevitable for restaurants to pre-empt what consumers will purchase, know their likings, predict what will work to run a restaurant effectively and this is where a wide-ranging restaurant analytics platform helps.

There are various predictive modeling tools and restaurant data analytics techniques that can help restaurant managers recognize key trends and sales opportunities to stay ahead of the competition. The whole process of restaurant analytics can be broken down into the following steps:

• Defining the objectives: The first step involves listing out the key problems for your business and finalizing the objectives for a given timeframe.
• Defining the metrics: In the second step, you will have to define the metrics that you will need to track the response you are getting for your restaurant. Some of the metrics include Staff Turnover Rate, Average Customer Wait Time, Channel ROI, and Social Engagement.
• Data collection: Data gets collected when orders are placed by customers when they share reviews on different platforms after purchase, feedback, and comments on social media, and more.
• Metric evaluation: After you have a set of well-defined objectives and a good stream of data from different platforms, you will be able to track your performance consistently. This will in turn help in studying trends and making decisions accordingly.

Here is a list of some of the major advantages of integrating analytics to restaurant management:

• It drives and controls position and visibility across all major regional restaurants.
• Restaurant managers will be able to plan and control promotions and sales campaigns in a more effective way.
• Generate actionable reports that identify problems within the restaurant service or chain.
• Restaurant analytics offers the planning and analysis needed to get total control of composite restaurant networks.
• Enhance operations by evaluating seating efficiencies and guest traffic.
• Simulate the influence of restaurant profitability and price changes.
• Reduce waste by predicting product usage.
• Augment suppliers’ relationships by issuing demand forecasts down to material level.
• Generate standardized records to benchmark specific restaurant/location performance against others and assess the global network.
• Recognize missed-revenue daily to check and subsequently protect the health of each restaurant.

Once a good stream of incoming data and metrics are in place, you will be able to start using this data for strategic purposes. The decisions you make will now be backed by solid insights and a strong analytics platform. The insights that restaurant analytics offers, support considerable sales growth without augmented advertising cost. Manthan offers newer ways to align analytics and customer to generate prospects for businesses.

AI-Driven Grocery Data Analytics

The grocery business is among the various retail categories that handle large volumes and require an efficient way to manage and track items across different categories. With Predictive Analytics that forecast trends based on looking at past and present-day data, grocery stores are getting an upper hand over the competition.

However, with many non-grocery players such as Amazon entering the grocery sector, it has become inevitable for the retail sector to adopt data-driven innovations such as AI-driven grocery store marketing tactics and grocery store analytics to stay on top of the competition. They can do so by leveraging their hard-earned data and gather insights from shopping preferences and consumer behavior.

What is the Artificial Intelligence (AI) Driven Approach?

With the AI-driven approach, all the available data will be processed and evaluated, and a decision will be made based on the connections between every single product that sheds light on aspects such as promotional and price elasticities.

For many, AI solutions will make the difference by discovering the best solutions to complex situations that need analyzing huge data sets. With the cost of AI technologies coming down and with rising computing capacity, grocers can make the most of the latest best practices when it comes to pricing, forecasting, and promotion.

Here is a list of business segments where AI-based analytics and predictive analytics can be used to help grocery store chains increase profitability:

Shopper Targeting

Customers are central to shopping and over time they form a pattern with granular data such as customer demographics. This can subsequently be utilised to the grocer’s advantage, for instance, to produce customized offers targeted explicitly at specific shoppers.

Pricing

Pricing has often been used as a tool to pull in customers. Predictive analytics can help retailers get answers to critical questions such as:

·         What is the right price point to enhance sales?

·         How would the sales increase with competitive pricing?

·         How frequently to introduce price-based promotional deals?

Many experts are certain that the use of Predictive Analytics starts showing results in just six months thus helping grocers make revenue gains.

AI analytics on the other hand can be used to sense buyer intent. Here is a list of sources of intent:

·         A favourite source of the intent signal is from knowing what a customer has searched for online. For instance, if a person has searched for gym shoes, there’s a good chance he/she is going to buy.

·         Other sources of buyer intent include what a customer is currently reading. For example, when someone starts reading about specific food brands, he/she is almost ready to purchase.

·         Lastly, the type of ads that individuals are clicking on can tell you what they are looking for.

The insights that AI offers, support substantial sales growth without amplified advertising cost. In the high-volume, low-margin grocery industry, it can prove to be very impactful. Manthan, offers newer ways to seamlessly align technology and customer to create opportunities for customer-obsessed businesses.

5 Reasons Why The Retail Industry Needs Merchandise Analytics

Retailers today are operating in an exceedingly competitive atmosphere. Apart from their brick-and-mortar rivals, they are also facing tough competition from ecommerce ventures that tech-savvy customers seem to prefer these days. The main challenge before them is meeting the exacting standards of their new-age customers while generating a healthy profit. With so many players in the market, achieving this is not a breeze. However, by utilizing the insights provided by merchandising analytics tools, retail establishments can devise various strategies to drive sales.

Here’s how merchandising analytics helps the retail industry : 

1. Analyzes Customer Behavior

 In order to cater to their customers’ needs, retailers must first find out what they want. Merchandising analytics tools collect and analyze customer data to provide insights into their behavior. They help retailers identify who are their loyal customers, what are their preferences, what motivates them to buy a particular product, and how and what is the best way to reach out to them. Equipped with these insights, retailers can create customer engagement plans like personalized promotions and special offers in certain categories, etc., to attract customers. 


2. Provides Demand Prediction

 Predictive merchandising analytics tools look into the different factors that influence the buying decision of customers like the latest trends, social media reviews, and weather etc., to provide a demand forecast of various items. This helps in optimizing inventory management. Based on these predictions, retailers could stock their inventory with those products that have the best chance of flying off the shelves, plan a store layout that highlights the in-demand products, and create targeted promotion campaigns to draw customers to their stores. 

3. Helps In Price Optimization

The pricing of products is directly related to the success of any business. Merchandising analytics can help retailers fix the right price for their goods by analyzing different factors such as demand, inventory levels, and competitor’s pricing, and the like. It is particularly helpful in determining the mark-down price of products during seasonal and flash sales. 

4. Tracks Product Performance

 By using data analytics tools, retailers can discover which products are selling fast and which are not performing well. This helps them in planning their inventory better and create strategies to boost the sale of under-performing products. Such insights also help in deciding which products should be offered at a discounted price during clearance sales.  5. Enables Faster Decision Making Retailers constantly need to think on their feet and take quick yet effective decisions to stay ahead of the game. By providing accurate information on customer preferences and market trends, retail analytics tools help them take prompt decisions regarding tracking, stocking, and restocking their products and crafting strategies to attract customers regularly. 

Retail Merchandising Analytics  are changing the way retailers do business. Global retail giants like Walmart are already using these tools to curb competition and accelerate growth. Do you also want to give your retail business a boost? Manthan, a leading merchandising analytics solutions provider, could help you achieve your goals. Visit their website for more information.   

CDP vs Data Management Platforms

What are the differences between a Customer Data Platform and a Data Management Platform? Both CDP and DMP are AI-powered analytics solutions. A CDP captures data of customers so that companies can use it to provide better and customized services and recommendations to their customers. A DMP on the other hand, is used mainly by the advertising industry. Data is collated which is then used for more customized and accurate targeting of customers to boost sales and create engaging content. There’s also a difference between the to when it comes to their data mechanism and system. CDP gathers data from first-party sources, whereas DMP gets the data from third-party sources. First-party data is data that’s collected directly from the customer itself. The data could have been derived from customer feedbacks or surveys. Third-party data is gathered via cookies, IP addresses etc.

DMPs are used to collect information about customers for categorizing customers and its information that’s short-lived and used for intermediary purposes. CDPs are based on concrete customer profiles and customer preferences and user habits. The information becomes a solid database which is a bankable asset to be utilised by companies. DMP is data that analyses the behaviour of a customer when it comes to the number of times, they’ve visited a website, and the information that they viewed. CDP is data that can assess whether a customer is a prospective customer for a company who will purchase their products or show an interest in it.

DMPs are used most prolifically in digital advertising industries and informs marketing strategies and identifying target audiences. The data captured from CDPs can be used on social media channels, for performance marketing etc. It even understands the social environment and the pulse of the social crowd.

So which daya system is more convenient for you? Which one is best suited to define customer marketing analytics for you? There are many factors that need to be taken into consideration when it comes to choosing between the two. You need to know what purpose you will be using the data systems for. What is your main goal? Who are you trying to reach? Is its real-time personalization or another key objective?

CDP is the most impactful for you if you’re in the advertising trade, because DMPs cannot adequately capture identities and customer profiles. With CDP, all the information is stored in one space, which makes it easy to utilize and analyse. DMP on the other hand stores its data in more than one place. CDP captures data and improves upon that data which is a big plus for companies in the long run. The data collated via a CDP can only be used in the short run.

All these points need to be considered when you want to make a choice between the two data systems.

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