Through process mining
, retailers gain the necessary understanding of their company's processes
to get the most out of their digital transformation efforts. He can then use
RPA and IPA to reduce the manual effort in his
organization as well as operating costs, process lead times and manual errors.
In particular, invoicing, price changes, liabilities, receivables as well as
inventory and / supply chain management can be automated without any problems.
ML-powered retail analytics can analyze the growing amount
of data businesses generate and extract actionable insights that can increase
sales while improving business performance. Embedded AI models can suggest
dynamic changes in various areas such as logistics, pricing or layout.
Conversational AI and chatbots are an
ideal solution for merchants to answer customer questions. If the chatbot cannot
serve the customer appropriately, it hands the customer over to an employee.
Retail chatbots increase sales and reduce costs through personalized, fast
messages.
Smart beacons, small devices that stick to objects or walls
in the store, connect to customers 'cell phones via Bluetooth signals and allow
the retailer to send messages to customers' phones about their promotions and
coupons.
On the trail of the customer with AI
tools
If retailers are focused on digital transformation, they
should implement the latest advances, including advances in AI. Computer Vision
offers enhanced security and plans the store layout for better customer
loyalty. In-store sensors collect data such as voice, images or videos in order
to better understand customers' habits and preferences. You can analyze the
in-store data to create heatmaps of your customers for optimizing store layout
and better upselling and cross-selling strategies. The evaluated data can also
be used to automate tasks that ensure that every customer always receives the
right advertising and that the associated products are not sold out. Real-time
customer analyzes can also help Plan future staffing requirements more
precisely and shorten queues at the POS. The automated analysis of historical
purchase data allows a forecast of stock requirements, which automatically
orders items before they are sold out. ML algorithms can create customer
journey maps to identify common customer issues, improve the customer
experience, and define what customers and scenarios need to complete a
purchase. READ MORE:- technologyies
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