Customer Lifetime Value Prediction Using Embeddings

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features

Pool boiling of water-Al2O3 and water-Cu nanofluids on horizontal smooth tubes

Experimental investigation of heat transfer during pool boiling of two nanofluids, i.e., water-Al2O3 and water-Cu has been carried out. Nanoparticles were tested at the concentration of 0.01%, 0.1%, and 1% by weight. The horizontal smooth copper and stainless steel tubes having 10 mm OD and 0.6 mm wall thickness formed test heater. The experiments have been performed to establish the influence of nanofluids concentration as well as tube surface material on heat transfer characteristics at atmospheric pressure. The results indicate that independent of concentration nanoparticle material (Al2O3 and Cu) has almost no influence on heat transfer coefficient while boiling of water-Al2O3 or water-Cu nanofluids on smooth copper tube. It seems that heater material did not affect the boiling heat transfer in 0.1 wt.% water-Cu nanofluid, nevertheless independent of concentration, distinctly higher heat transfer coefficient was recorded for stainless steel tube than for copper tube for the same heat flux density

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

Incorporating field wind data to improve crop evapotranspiration parameterization in heterogeneous regions

Accurate parameterization of reference evapotranspiration ( ET0) is necessary for optimizing irrigation scheduling and avoiding costs associated with over-irrigation (water expense, loss of water productivity, energy costs, and pollution) or with under-irrigation (crop stress and suboptimal yields or quality). ET0 is often estimated using the FAO-56 method with meteorological data gathered over a reference surface, usually short grass. However, the density of suitable ET0 stations is often low relative to the microclimatic variability of many arid and semi-arid regions, leading to a potentially inaccurate ET0 for irrigation scheduling. In this study, we investigated multiple ET0 products from six meteorological stations, a satellite ET0 product, and integration (merger) of two stations’ data in Southern California, USA. We evaluated ET0 against lysimetric ET observations from two lysimeter systems (weighing and volumetric) and two crops (wine grapes and Jerusalem artichoke) by calculating crop ET ( ETc) using crop coefficients for the lysimetric crops with the different ET0. ETc calculated with ET0 products that incorporated field-specific wind speed had closer agreement with lysimetric ET, with RMSE reduced by 36 and 45% for grape and Jerusalem artichoke, respectively, with on-field anemometer data compared to wind data from the nearest station. The results indicate the potential importance of on-site meteorological sensors for ET0 parameterization; particularly where microclimates are highly variable and/or irrigation water is expensive or scarce

The impact of local networks on subsistence resilience and biodiversity in a low-lying Moluccan reef system between 1600 and the present

Using field data for the 1980s and historical material, I show how the central places of networks crucial for regional and long-distance trade in the Moluccas between 1600 and the present were often environmentally vulnerable volcanic islands and low-lying reefs. After reviewing existing data on hazards, and evaluating the evidence for erosion and degradation, I suggest how resilience has been historically achieved through social and material exchanges between islands, accommodating the consequences of specific perturbations. Re-interpretation of published data shows how inter-island trade has re-organised patterns of biological interaction spatially and over the long-term, helping us assesses whether in the face of climate change effects such areas are zones of robustness or of potential fragility

What is the value of experimentation and measurement?

Experimentation and Measurement (E&M) capabilities allow organizations to accurately assess the impact of new propositions and to experiment with many variants of existing products. However, until now, the question of measuring the measurer, or valuing the contribution of an E&M capability to organizational success has not been addressed. We tackle this problem by analyzing how, by decreasing estimation uncertainty, E&M platforms allow for better prioritization. We quantify this benefit in terms of expected relative improvement in the performance of all new propositions and provide guidance for how much an E&M capability is worth and when organizations should invest in one

Customer life time value prediction using embeddings

We describe the Customer Life Time Value (CLTV) prediction sys- tem deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accu- rate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses.The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evalu- ate customer loyalty. We describe our system, which adopts this approach, and our ongoing e orts to further improve it. Recently, domains including language, vision and speech have shown dra- matic advances by replacing hand-crafted features with features that are learned automatically from data. We show that learning feature representations is a promising extension to the state of the art in CLTV modeling. We propose a novel way to generate embed- dings of customers which addresses the issue of the ever changing product catalogue and obtain a signi cant improvement over an exhaustive set of handcrafted features

Improvement of size dispersion in CdTe quantum dots in glass by using double annealing process

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Shell-core CdTeS quantum dots in glass

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